Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Correspondingly, feature compensation is computed by incorporating the context into the existing representation while a soft gating is applied to highlight the degree that each data example needs transferring, where both the context-directed feature compensation and the soft gating are learnt end to end;

The review comment to be evaluated is:

Strengths:
- The paper tackles an important problem of identifying the mismatch between a source domain and a target domain before transfer.
- The paper is, in general, well-aligned with literature in the area.

Weaknesses:
- The paper does not provide explanations / intuitions to most technical decisions. For instance, the proposed method is very quickly explained in roughly half a page (sections 3.1 - 3.2). For example, why is it reasonable to use DEC? Why is compensation necessary - what are the underlying factors that make it necessary once the closest cluster centers have been identified? Why is cosine similarity, and no other measure, applicable in this case?
- It is very unclear to me how the proposed method is actually helping to identify the mismatch between a source domain and a target domain. In several parts of the paper, it is mentioned that the cluster centers are calculated on the full training set, and according to my understanding it is the whole training set of the target domain for which cluster centers are being calculated? I think it would greatly benefit the explanation of the method if it would clearly distinguish which parts of it come from the source domain and which from the target domain.
- In the experiments, I do not see any reasoning as to what the proposed method works in some cases but not in others. In which cases doing this kind of clustering + compensation approach would work better? This kind of description is very important to really see the contribution of the proposed method to the problem of transfer learning.

The evidence to be evaluated is:GTN (Zhu et al., 2018) holds in terms of feature selection to weight features with gating neural network such that the common features shared by source domain and target domain can receive more attention.

In contrast to (Guo et al., 2020; Liu et al., 2021), our method can approach this goal since we just add a tiny network to obtain enhanced feature to compensate for the feature from backbone network.

Then, we compute the similarity between the input data example and each of the centroids resulting from clustering so as to figure out the position of the input in reference to the cluster centers, where the cluster centers act as anchors to figure out data distribution as well as how far from the input to each anchor.

Then, we compute the cosine similarity between \(\mathbf{X}\) and \(\mathbf{C}_{1},\mathbf{C}_{2},\ldots,\mathbf{C}_{K}\) respectively, and the corresponding similarity is denoted as \(\mathbf{S}=(\mathbf{S}_{1},\mathbf{S}_{2},\ldots,\mathbf{S}_{K})\).

These similarities are referred to as context since they figure out the relative position of \(\mathbf{X}\) in regard to the cluster centers, based on which the compensation to the original feature embedding is computed.

The first branch appends the context vector \(\mathbf{S}\) to the original embedding \(\mathbf{X}\) via concatenation, and then a multilayer perceptron (MLP) is used to transform \(\mathbf{X}\)()S into \(\Delta=MLP(\mathbf{X}\)()S), where MLP is designed to force \(\Delta\) has the same dimension as \(\mathbf{X}\), and () denotes concatenation. The second branch computes the soft gating factor \(\mathbf{W}=MLP(\mathbf{S})\) so as to obtain the weighted compensation subject to the context, that is, \(\mathbf{W}.\Delta\), where \(\mathbf{W}\) is a scalar.

The transfer network to produce \(\mathbf{W}.\Delta=MLP(\mathbf{S}).MLP(\mathbf{X}\)()S) is trained end to end.

In Eq. (1), we concatenate \(\mathbf{X}\) and \(\mathbf{S}\) to produce the compensation \(MLP(\mathbf{X}(\mathbf{\bar{\odot}}\mathbf{S})\) for transferring feature embedding because \(\mathbf{S}\) indicates how ambiguous \(\mathbf{X}\) is in terms of being associate with a deterministic cluster, say, context. Moreover, the soft weighting \(MLP(\mathbf{S})\) aims to control the overall compensation adaptive to the context \(\mathbf{S}\). It is apparent that anchors resulting from clustering determines the performance of the proposed solution since they are the base to figure out data distribution and aware of the contextual clue to produce compensation adaptively. Here, we make use of the algorithm referred to as Deep Embedded Clustering (DEC) (Xie et al., 2016) to obtain such anchor points, and the experiments confirm that DEC performs very well in the proposed framework.

* (II) Train the backbone and transfer network as a whole end to end.

* GTN (Zhu et al., 2018): Gated Transfer Network is used to weight features from pre-trained model, which works in the sense of feature selection.

Here, the color of each pixel corresponds with the corresponding soft gating factor \(\mathbf{W}\), and a higher heat value of \(\mathbf{W}\) means a bigger compensation applied to transfer the feature embedding. We can see that bigger compensations are mostly applied to the data points locating in a self-contained region. This coincides with the principle that transfer learning is in general focused on such data examples that are incompatible with the target domain. Figure 2(b) shows the 2-dimensional histogram of maximum cosine similarity against soft gating factor \(\mathbf{W}\), which shows such a trend that lower similarity corresponds with in general higher compensation level, say, higher soft gating. Since the maximum similarity to cluster centers indicates the membership degree of the data example in association with a deterministic cluster, and a smaller value means a higher ambiguous case in terms of membership association, it is thus natural to result in higher-level transferring for feature compensation.

We propose a new transfer learning method with context-aware compensation to feature embedding, where a contextual descriptor figuring out how close from the input to each cluster center is applied, and soft gating is used to make the feature transfer focused on the data samples that truly need transferring.

Delta: Deep learning transfer using feature map with attention for convolutional networks.

Input: Figure 2: (a) Visualization of the data distribution of Stanford Dogs data via PCA. The heat value of each pixel corresponds with the value of the soft gating factor W in association with this pixel. (b) 2-dimensional histogram of maximum cosine similarity against soft gating factor (W ).

Input: Figure 1: Context-directed Transfer Learning

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find the claim to be moderately well-stated but with some degree of overstatement. While the evidence demonstrates that the method does incorporate context through cosine similarity with cluster centers and applies soft gating for feature compensation, several key aspects warrant consideration:

1. The evidence shows a detailed architecture and mathematical framework supporting the basic mechanism described in the claim.
2. The visualization in Figure 2 provides empirical support for the soft gating behavior.
3. However, as noted in the review comments, there's limited explanation of why certain technical decisions were made and how the method specifically helps identify domain mismatch.
4. The claim could better acknowledge the role of DEC clustering and the specific similarity measure used.

The claim is somewhat overstated in terms of the implied seamlessness of the "end-to-end" learning, though the core technical description is accurate.

<score>2</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Correspondingly, feature compensation is computed by incorporating the context into the existing representation while a soft gating is applied to highlight the degree that each data example needs transferring, where both the context-directed feature compensation and the soft gating are learnt end to end;

The review comment to be evaluated is:

### Strength

- The method is interesting. Studying the contextual relation between source and target domains is novel.
- The related work section is sufficient. So much literature is surveyed.
- Experimental datasets and comparison methods are representative.

### Weakness

- Although the idea of learning contextual relation is novel, this approach is not. It remains unclear whether clustering can model the context information, or if the results of clusters and original inputs can solve the mismatch between source and target domains in transfer learning.
- The claim that "this method only adds two MLPs to the backbone network" is not correct. As can be seen from Figure 1, there are more than two MLPs: it still has an autoencoder (DEC), which is much more complicated than MLPs.
- The method part is extremely confusing and not clear, which should be rewritten. I find figure 1 hard to understand, specifically how to compute $\mathbf{X} \textcopyright \mathbf{S}$ and $\mathbf{W} \cdot \Delta$. (I even think that should be $\cdot$ instead of $.$, since you claim it's a product.)
- The success of "compensation" heavily relies on DEC (deep embedding clustering). If DEC does not give the correct clustering results, the method will fail. How to justify this? Additionally, DEC brings extra computations, which should be highlighted in the paper.
- This method will consume a lot of training time than comparison methods, according to Sec. 3.3. We see that there is more than one step of fine-tuning, which is more complicated than existing work. Authors should compare your training and inference time in the experiments.
- In experiments, you should also list the number of training parameters of each method for a more fair study since this method introduces extra neural network modules.
- More importantly, the performance on general fine-tuning tasks of this method is NOT good. According to table 1, This approach only succeeds using Resnet-101, while it does not generate competitive results in Resnet-50 and VGG16. Hence, it is difficult to say that this approach actually works in different backbones.
- No experiments to explain why this method works. Lack of detailed analysis.
- With regards to related work, I think this paper misses two very important references on the same topic:

[1] Jang Y, Lee H, Hwang S J, et al. Learning what and where to transfer[C]//International Conference on Machine Learning. PMLR, 2019: 3030-3039.

[2] Murugesan K, Sadashivaiah V, Luss R, et al. Auto-Transfer: Learning to Route Transferrable Representations[J]. arXiv preprint arXiv:2202.01011, 2022.

The evidence to be evaluated is:GTN (Zhu et al., 2018) holds in terms of feature selection to weight features with gating neural network such that the common features shared by source domain and target domain can receive more attention.

In contrast to (Guo et al., 2020; Liu et al., 2021), our method can approach this goal since we just add a tiny network to obtain enhanced feature to compensate for the feature from backbone network.

Then, we compute the similarity between the input data example and each of the centroids resulting from clustering so as to figure out the position of the input in reference to the cluster centers, where the cluster centers act as anchors to figure out data distribution as well as how far from the input to each anchor.

Then, we compute the cosine similarity between \(\mathbf{X}\) and \(\mathbf{C}_{1},\mathbf{C}_{2},\ldots,\mathbf{C}_{K}\) respectively, and the corresponding similarity is denoted as \(\mathbf{S}=(\mathbf{S}_{1},\mathbf{S}_{2},\ldots,\mathbf{S}_{K})\).

These similarities are referred to as context since they figure out the relative position of \(\mathbf{X}\) in regard to the cluster centers, based on which the compensation to the original feature embedding is computed.

The first branch appends the context vector \(\mathbf{S}\) to the original embedding \(\mathbf{X}\) via concatenation, and then a multilayer perceptron (MLP) is used to transform \(\mathbf{X}\)()S into \(\Delta=MLP(\mathbf{X}\)()S), where MLP is designed to force \(\Delta\) has the same dimension as \(\mathbf{X}\), and () denotes concatenation. The second branch computes the soft gating factor \(\mathbf{W}=MLP(\mathbf{S})\) so as to obtain the weighted compensation subject to the context, that is, \(\mathbf{W}.\Delta\), where \(\mathbf{W}\) is a scalar.

The transfer network to produce \(\mathbf{W}.\Delta=MLP(\mathbf{S}).MLP(\mathbf{X}\)()S) is trained end to end.

In Eq. (1), we concatenate \(\mathbf{X}\) and \(\mathbf{S}\) to produce the compensation \(MLP(\mathbf{X}(\mathbf{\bar{\odot}}\mathbf{S})\) for transferring feature embedding because \(\mathbf{S}\) indicates how ambiguous \(\mathbf{X}\) is in terms of being associate with a deterministic cluster, say, context. Moreover, the soft weighting \(MLP(\mathbf{S})\) aims to control the overall compensation adaptive to the context \(\mathbf{S}\). It is apparent that anchors resulting from clustering determines the performance of the proposed solution since they are the base to figure out data distribution and aware of the contextual clue to produce compensation adaptively. Here, we make use of the algorithm referred to as Deep Embedded Clustering (DEC) (Xie et al., 2016) to obtain such anchor points, and the experiments confirm that DEC performs very well in the proposed framework.

* (II) Train the backbone and transfer network as a whole end to end.

* GTN (Zhu et al., 2018): Gated Transfer Network is used to weight features from pre-trained model, which works in the sense of feature selection.

Here, the color of each pixel corresponds with the corresponding soft gating factor \(\mathbf{W}\), and a higher heat value of \(\mathbf{W}\) means a bigger compensation applied to transfer the feature embedding. We can see that bigger compensations are mostly applied to the data points locating in a self-contained region. This coincides with the principle that transfer learning is in general focused on such data examples that are incompatible with the target domain. Figure 2(b) shows the 2-dimensional histogram of maximum cosine similarity against soft gating factor \(\mathbf{W}\), which shows such a trend that lower similarity corresponds with in general higher compensation level, say, higher soft gating. Since the maximum similarity to cluster centers indicates the membership degree of the data example in association with a deterministic cluster, and a smaller value means a higher ambiguous case in terms of membership association, it is thus natural to result in higher-level transferring for feature compensation.

We propose a new transfer learning method with context-aware compensation to feature embedding, where a contextual descriptor figuring out how close from the input to each cluster center is applied, and soft gating is used to make the feature transfer focused on the data samples that truly need transferring.

Delta: Deep learning transfer using feature map with attention for convolutional networks.

Input: Figure 2: (a) Visualization of the data distribution of Stanford Dogs data via PCA. The heat value of each pixel corresponds with the value of the soft gating factor W in association with this pixel. (b) 2-dimensional histogram of maximum cosine similarity against soft gating factor (W ).

Input: Figure 1: Context-directed Transfer Learning

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim, evidence, and reviewer comments, I find the claim to be moderately overstated. While the evidence does show the method incorporates context and applies soft gating for feature compensation, several key issues arise:

1. The reviewer points out that clustering's effectiveness for modeling context information remains unclear
2. The actual implementation is more complex than suggested, involving an autoencoder (DEC) beyond just MLPs
3. The success heavily depends on DEC's clustering performance, which isn't acknowledged in the claim
4. The methodology figures show a more complicated architecture than the claim implies

The claim oversimplifies the approach and doesn't acknowledge important limitations or dependencies, though the core technical description is factually accurate.

<score>4</score>

Justification:
- The claim accurately describes the high-level concept but overstates the simplicity and directness of the approach
- Evidence shows additional complexity with DEC and multiple training steps not mentioned in the claim
- Reviewer highlights valid concerns about clustering effectiveness and dependencies
- Figure 1 reveals a more complex architecture than suggested by the claim's wording
- The claim lacks important caveats about the method's dependencies and limitations


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Correspondingly, feature compensation is computed by incorporating the context into the existing representation while a soft gating is applied to highlight the degree that each data example needs transferring, where both the context-directed feature compensation and the soft gating are learnt end to end;

The review comment to be evaluated is:

Strengths:
the paper is well written, methodology is well-described, and the problem addressed is relevant.

Weaknesses.
The author is describing how to use data centers to do data compensation, however, how this is related to data transferring is poorly described. For examples, is data centers computed on target data set? how is its feature vector computed, using the pretrained network? using distance to get compensation factor seems only to be working under a strong assumption that this is used for classification, and it only works for image classification tasks? why using distance to centers work? all these questions are not answered properly.

Based on the experimental setup, it is just finetuning the pretrained network using some downstream tasks. It just barely competes with SOTA methods, seems that with or without such 2 layers of compensation networks does not change the result at all.

The evidence to be evaluated is:GTN (Zhu et al., 2018) holds in terms of feature selection to weight features with gating neural network such that the common features shared by source domain and target domain can receive more attention.

In contrast to (Guo et al., 2020; Liu et al., 2021), our method can approach this goal since we just add a tiny network to obtain enhanced feature to compensate for the feature from backbone network.

Then, we compute the similarity between the input data example and each of the centroids resulting from clustering so as to figure out the position of the input in reference to the cluster centers, where the cluster centers act as anchors to figure out data distribution as well as how far from the input to each anchor.

Then, we compute the cosine similarity between \(\mathbf{X}\) and \(\mathbf{C}_{1},\mathbf{C}_{2},\ldots,\mathbf{C}_{K}\) respectively, and the corresponding similarity is denoted as \(\mathbf{S}=(\mathbf{S}_{1},\mathbf{S}_{2},\ldots,\mathbf{S}_{K})\).

These similarities are referred to as context since they figure out the relative position of \(\mathbf{X}\) in regard to the cluster centers, based on which the compensation to the original feature embedding is computed.

The first branch appends the context vector \(\mathbf{S}\) to the original embedding \(\mathbf{X}\) via concatenation, and then a multilayer perceptron (MLP) is used to transform \(\mathbf{X}\)()S into \(\Delta=MLP(\mathbf{X}\)()S), where MLP is designed to force \(\Delta\) has the same dimension as \(\mathbf{X}\), and () denotes concatenation. The second branch computes the soft gating factor \(\mathbf{W}=MLP(\mathbf{S})\) so as to obtain the weighted compensation subject to the context, that is, \(\mathbf{W}.\Delta\), where \(\mathbf{W}\) is a scalar.

The transfer network to produce \(\mathbf{W}.\Delta=MLP(\mathbf{S}).MLP(\mathbf{X}\)()S) is trained end to end.

In Eq. (1), we concatenate \(\mathbf{X}\) and \(\mathbf{S}\) to produce the compensation \(MLP(\mathbf{X}(\mathbf{\bar{\odot}}\mathbf{S})\) for transferring feature embedding because \(\mathbf{S}\) indicates how ambiguous \(\mathbf{X}\) is in terms of being associate with a deterministic cluster, say, context. Moreover, the soft weighting \(MLP(\mathbf{S})\) aims to control the overall compensation adaptive to the context \(\mathbf{S}\). It is apparent that anchors resulting from clustering determines the performance of the proposed solution since they are the base to figure out data distribution and aware of the contextual clue to produce compensation adaptively. Here, we make use of the algorithm referred to as Deep Embedded Clustering (DEC) (Xie et al., 2016) to obtain such anchor points, and the experiments confirm that DEC performs very well in the proposed framework.

* (II) Train the backbone and transfer network as a whole end to end.

* GTN (Zhu et al., 2018): Gated Transfer Network is used to weight features from pre-trained model, which works in the sense of feature selection.

Here, the color of each pixel corresponds with the corresponding soft gating factor \(\mathbf{W}\), and a higher heat value of \(\mathbf{W}\) means a bigger compensation applied to transfer the feature embedding. We can see that bigger compensations are mostly applied to the data points locating in a self-contained region. This coincides with the principle that transfer learning is in general focused on such data examples that are incompatible with the target domain. Figure 2(b) shows the 2-dimensional histogram of maximum cosine similarity against soft gating factor \(\mathbf{W}\), which shows such a trend that lower similarity corresponds with in general higher compensation level, say, higher soft gating. Since the maximum similarity to cluster centers indicates the membership degree of the data example in association with a deterministic cluster, and a smaller value means a higher ambiguous case in terms of membership association, it is thus natural to result in higher-level transferring for feature compensation.

We propose a new transfer learning method with context-aware compensation to feature embedding, where a contextual descriptor figuring out how close from the input to each cluster center is applied, and soft gating is used to make the feature transfer focused on the data samples that truly need transferring.

Delta: Deep learning transfer using feature map with attention for convolutional networks.

Input: Figure 2: (a) Visualization of the data distribution of Stanford Dogs data via PCA. The heat value of each pixel corresponds with the value of the soft gating factor W in association with this pixel. (b) 2-dimensional histogram of maximum cosine similarity against soft gating factor (W ).

Input: Figure 1: Context-directed Transfer Learning

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, the claim appears to be moderately overstated based on several key factors:

1. The evidence shows a basic architecture for feature compensation using context and soft gating, but the claim overstates the seamlessness of the "end-to-end" learning process. The reviewer points out that it "barely competes with SOTA methods."

2. While the diagrams and equations demonstrate the technical implementation, the key concerns raised by the reviewer about how data centers relate to transfer learning and why distance-based compensation works aren't adequately addressed in the evidence.

3. The evidence shows some empirical support through visualizations (Fig 2), but doesn't fully justify the effectiveness of the approach compared to alternatives.

4. The claim makes broad statements about feature compensation and transfer learning without acknowledging important limitations noted in the review (e.g., assumptions about classification tasks).

<score>4</score>

The claim would be better stated by acknowledging limitations and being more precise about the specific conditions under which the approach works effectively.


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

This acts as the context to indicate whether and how much feature compensation is needed for the input to compensate for the mismatch between source domain and target domain.

The evidence to be evaluated is:**Transfer learning** aims to transfer the learnt knowledge from source domain to target domain (Thrun, 1995) so as to allow pre-trained models reused with minimum effort and good transferability.

Mapping-based deep transfer learning maps the source domain and target domain to a new space, in which they could be coherent to each other to some extent, and model is trained in the new space.

Domain Adversarial Neural Network (DANN) (Ajakan et al., 2014) introduces the idea of GAN to transfer learning by applying a domain classifier to minimize the difference between source and target domain in feature learning, that is, optimization on transferred feature representation is driven by not only classification but also removing the gap between source and target domain.

Fine tune is limited in that the difference between source domain and target domain is not taken into account. In view of that, \(L^{2}\)-SP (Xuhong et al., 2018) and DELTA (Li et al., 2019) apply regularization to loss function in order to compensate for such difference. GTN (Zhu et al., 2018) holds in terms of feature selection to weight features with gating neural network such that the common features shared by source domain and target domain can receive more attention.

From another point of view, parameter modification on the original network (Xuhong et al., 2018; Li et al., 2019; Zhu et al., 2018; Zhong & Maki, 2020) is not able to tackle all mismatches between source and target domain. Hereafter, AdaFilter (Guo et al., 2020) and TransTailor (Liu et al., 2021) alleviate this by reorganizing the network architecture.

In contrast to (Guo et al., 2020; Liu et al., 2021), our method can approach this goal since we just add a tiny network to obtain enhanced feature to compensate for the feature from backbone network.

Therefore, we first perform clustering on training data to obtain the centers representing data distribution. Then, we compute the similarity between the input data example and each of the centroids resulting from clustering so as to figure out the position of the input in reference to the cluster centers, where the cluster centers act as anchors to figure out data distribution as well as how far from the input to each anchor. We denote the \(K\) cluster centroids as \(\mathbf{C}=(\mathbf{C}_{1},\mathbf{C}_{2},\ldots,\mathbf{C}_{K})\) and the input as \(\mathbf{X}\). Then, we compute the cosine similarity between \(\mathbf{X}\) and \(\mathbf{C}_{1},\mathbf{C}_{2},\ldots,\mathbf{C}_{K}\) respectively, and the corresponding similarity is denoted as \(\mathbf{S}=(\mathbf{S}_{1},\mathbf{S}_{2},\ldots,\mathbf{S}_{K})\). Note that \(\mathbf{X}\) should undergo Auto-Encoder based representation learning prior to clustering and similarity computing, as required by the clustering module applied in this study, namely, Deep Embedded Clustering (DEC) (Xie et al., 2016). These similarities are referred to as context since they figure out the relative position of \(\mathbf{X}\) in regard to the cluster centers, based on which the compensation to the original feature embedding is computed. As shown in Figure 1, the compensation to the original embedding is subject to two branches: The first branch appends the context vector \(\mathbf{S}\) to the original embedding \(\mathbf{X}\) via concatenation, and then a multilayer perceptron (MLP) is used to transform \(\mathbf{X}\)()S into \(\Delta=MLP(\mathbf{X}\)()S), where MLP is designed to force \(\Delta\) has the same dimension as \(\mathbf{X}\), and () denotes concatenation. The second branch computes the soft gating factor \(\mathbf{W}=MLP(\mathbf{S})\) so as to obtain the weighted compensation subject to the context, that is, \(\mathbf{W}.\Delta\), where \(\mathbf{W}\) is a scalar.

In Eq. (1), we concatenate \(\mathbf{X}\) and \(\mathbf{S}\) to produce the compensation \(MLP(\mathbf{X}(\mathbf{\bar{\odot}}\mathbf{S})\) for transferring feature embedding because \(\mathbf{S}\) indicates how ambiguous \(\mathbf{X}\) is in terms of being associate with a deterministic cluster, say, context. Moreover, the soft weighting \(MLP(\mathbf{S})\) aims to control the overall compensation adaptive to the context \(\mathbf{S}\). It is apparent that anchors resulting from clustering determines the performance of the proposed solution since they are the base to figure out data distribution and aware of the contextual clue to produce compensation adaptively. Here, we make use of the algorithm referred to as Deep Embedded Clustering (DEC) (Xie et al., 2016) to obtain such anchor points, and the experiments confirm that DEC performs very well in the proposed framework.

* GTN (Zhu et al., 2018): Gated Transfer Network is used to weight features from pre-trained model, which works in the sense of feature selection.

* DELTA (Li et al., 2019): DELTA characterizes the distance between source/target networks using their outer layer outputs, and incorporates such distance as the regularization term of the loss function.

* Co-Tuning (You et al., 2020): The pre-trained model is fine-tuned using ground truth labels and probabilistic labels with learned relationship between source categories and target categories.

For MIT Indoor-67 data, the target is scene classification, which differs much from the source domain, say, object recognition.

Note that we annotate \(L^{2}\), \(L^{2}\)-SP and DELTA with * in Table 1 as they use Places 365 (Zhou et al., 2017) as source-domain data for pre-training, which is also a scene classification data. As a result, their performance seems more promising since the source domain and the target domain are focused on the same visual recognition task.

Here, the color of each pixel corresponds with the corresponding soft gating factor \(\mathbf{W}\), and a higher heat value of \(\mathbf{W}\) means a bigger compensation applied to transfer the feature embedding. We can see that bigger compensations are mostly applied to the data points locating in a self-contained region. This coincides with the principle that transfer learning is in general focused on such data examples that are incompatible with the target domain. Figure 2(b) shows the 2-dimensional histogram of maximum cosine similarity against soft gating factor \(\mathbf{W}\), which shows such a trend that lower similarity corresponds with in general higher compensation level, say, higher soft gating. Since the maximum similarity to cluster centers indicates the membership degree of the data example in association with a deterministic cluster, and a smaller value means a higher ambiguous case in terms of membership association, it is thus natural to result in higher-level transferring for feature compensation. This fully exhibits the motivation of this study, that is, why we make use of clustering centers as the anchors to figure out the contextual clue in directing feature compensation.

We propose a new transfer learning method with context-aware compensation to feature embedding, where a contextual descriptor figuring out how close from the input to each cluster center is applied, and soft gating is used to make the feature transfer focused on the data samples that truly need transferring. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline Backbone & Method & Caltech 256-30 & Caltech 256-60 & CUB-200 & Stanford Dogs & MIT Indoor-67 \\ \hline \multirow{6}{*}{ResNet101} & TransTailor & 85.3 & 87.3 & 80.7 & 91 & 78.2 \\  & PrtR & 84.5 & 87.2 & - & - & 79.2 \\  & \(L^{2}\) & 81.5±0.2 & 85.3±0.2 & - & 81.4±0.2 & 79.6±0.5* \\  & \(L^{2}\)-SP & 83.5±0.1 & 86.4±0.2 & - & 85.1±0.2 & 84.2±0.3* \\  & DELTA & 86.6 & **88.7** & 80.5 & 88.7 & 85.5* \\  & Fine tune & 82.65 & 85.91 & 83.7 & 91.17 & **80.82** \\  & Ours (I) & 83.43 & 86.36 & 83.91 & **92.24** & 80.22 \\  & Ours (II) & 83.02 & 85.27 & **84.89** & 91.75 & 80.52 \\  & Ours (I+) & **86.62** & 86.66 & - & - & - \\ \hline \multirow{6}{*}{ResNet50} & PrtR & **83.9** & **87.1** & 81.9 & - & 77.9 \\  & AdaFilter & 80.62 & 84.31 & - & 82.44 & 77.53 \\  & GTN & - & - & 79.95 & - & 77.77 \\  & \(L^{2}\)-SP & - & - & 78.44 & 86.72 & - \\  & DELTA & - & - & 78.63 & 86.01 & - \\  & BSS & - & - & 78.85 & 87.18 & - \\  & StochNorm & - & - & 79.58 & - & - \\  & Co-Tuning & - & - & 81.24 & - & - \\  & Fine tune & 79.94 & 83.04 & 83.83 & 89.79 & **79.55** \\  & Ours (I) & 80.58 & 83.39 & **83.98** & 90.5 & 79.4 \\  & Ours (II) & 81.09 & 83.73 & 83.93 & **90.92** & 79.62 \\  & Ours (I+) & 83.32 & 83.96 & - & - & - \\ \hline \multirow{6}{*}{VGG16} & TransTailor & **76.4** & **81.8** & 79.2 & **84.2** & **76.5** \\  & PrtR & - & - & 75.1 & - & 73 \\ \cline{1-1}  & GTN & - & - & 72.14 & - & 71.19 \\ \cline{1-1}  & Fine tune & 54.06 & 59.14 & 76.57 & 75.5 & 72.91 \\ \cline{1-1}  & Ours (I) & 52.61 & 58.47 & 76.51 & 75.81 & 72.23 \\ \cline{1-1}  & Ours (II) & 60.46 & 66.42 & **80.3** & 77.65 & 75.59 \\ \cline{1-1}  & Ours (III) & 66.46 & 70.66 & 64.84 & 83.39 & 71.34 \\ \cline{1-1}  & Ours (I+) & 63.37 & 66.28 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification precision (%) compared with baselines on 5 data sets

Input: Table 1: Classification precision (%) compared with baselines on 5 data sets

Input: Figure 2: (a) Visualization of the data distribution of Stanford Dogs data via PCA. The heat value of each pixel corresponds with the value of the soft gating factor W in association with this pixel. (b) 2-dimensional histogram of maximum cosine similarity against soft gating factor (W ).

Input: Figure 1: Context-directed Transfer Learning

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
I will assess the claim: "This acts as the context to indicate whether and how much feature compensation is needed for the input to compensate for the mismatch between source domain and target domain."

Summary of evaluation:
The evidence strongly supports this claim through:
1. Detailed mathematical description of how cluster-based context (S1,...SK) informs feature compensation
2. Experimental validation shown in Figure 2 demonstrating correlation between similarity scores and compensation levels
3. Visual results in Figure 2(a) showing compensation being applied adaptively based on data distribution
4. Strong performance results in Table 1 validating the effectiveness of the context-based approach

The claim is well-stated and accurately reflects the evidence without exaggeration. The paper provides thorough theoretical foundation, implementation details, and empirical validation of how the contextual information guides feature compensation.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

This acts as the context to indicate whether and how much feature compensation is needed for the input to compensate for the mismatch between source domain and target domain.

The review comment to be evaluated is:

Strengths:
- The problem addressed by the paper is relevant.
- The methodology is clear and well-described.
- The experiments are fair and show that the method is competitive with SOTA in some cases.

Weaknesses:
1. In the introduction, the authors remark that "due to the overlap between the source domain and the target domain, not every data example needs transferring, and how much transferring is needed for a data example is subject to its position in the overall data distribution". Nonetheless, in their algorithm, clustering is performed using the target data. Thus, the computed similarities will not tell how far a given target example is from the source distribution. Therefore, I don't see how they are useful to assess "how much transferring is needed" for that example. When the source distribution is very different from the target, the method seems inappropriate. The poor results on MIT Indoor-67 reinforce this suspicion.
2. Are the displacements $\Delta$ normalized? Otherwise, the role of the predicted magnitude $W$ is redundant.
3. The experimental results are not particularly convincing. In particular, it is unclear which of the training strategies proposed in section 3.3 is the best one. In addition, it would be interesting to visualize what kind of transformations the model is learning, i.e. if it's learning $\Delta$s that pull representations towards the cluster centroids (or something else).
4. A theoretical analysis of the proposed methodology is missing. It is presented as a mere heuristic without any theoretical guarantees.

The evidence to be evaluated is:**Transfer learning** aims to transfer the learnt knowledge from source domain to target domain (Thrun, 1995) so as to allow pre-trained models reused with minimum effort and good transferability.

Mapping-based deep transfer learning maps the source domain and target domain to a new space, in which they could be coherent to each other to some extent, and model is trained in the new space.

Domain Adversarial Neural Network (DANN) (Ajakan et al., 2014) introduces the idea of GAN to transfer learning by applying a domain classifier to minimize the difference between source and target domain in feature learning, that is, optimization on transferred feature representation is driven by not only classification but also removing the gap between source and target domain.

Fine tune is limited in that the difference between source domain and target domain is not taken into account. In view of that, \(L^{2}\)-SP (Xuhong et al., 2018) and DELTA (Li et al., 2019) apply regularization to loss function in order to compensate for such difference. GTN (Zhu et al., 2018) holds in terms of feature selection to weight features with gating neural network such that the common features shared by source domain and target domain can receive more attention.

From another point of view, parameter modification on the original network (Xuhong et al., 2018; Li et al., 2019; Zhu et al., 2018; Zhong & Maki, 2020) is not able to tackle all mismatches between source and target domain. Hereafter, AdaFilter (Guo et al., 2020) and TransTailor (Liu et al., 2021) alleviate this by reorganizing the network architecture.

In contrast to (Guo et al., 2020; Liu et al., 2021), our method can approach this goal since we just add a tiny network to obtain enhanced feature to compensate for the feature from backbone network.

Therefore, we first perform clustering on training data to obtain the centers representing data distribution. Then, we compute the similarity between the input data example and each of the centroids resulting from clustering so as to figure out the position of the input in reference to the cluster centers, where the cluster centers act as anchors to figure out data distribution as well as how far from the input to each anchor. We denote the \(K\) cluster centroids as \(\mathbf{C}=(\mathbf{C}_{1},\mathbf{C}_{2},\ldots,\mathbf{C}_{K})\) and the input as \(\mathbf{X}\). Then, we compute the cosine similarity between \(\mathbf{X}\) and \(\mathbf{C}_{1},\mathbf{C}_{2},\ldots,\mathbf{C}_{K}\) respectively, and the corresponding similarity is denoted as \(\mathbf{S}=(\mathbf{S}_{1},\mathbf{S}_{2},\ldots,\mathbf{S}_{K})\). Note that \(\mathbf{X}\) should undergo Auto-Encoder based representation learning prior to clustering and similarity computing, as required by the clustering module applied in this study, namely, Deep Embedded Clustering (DEC) (Xie et al., 2016). These similarities are referred to as context since they figure out the relative position of \(\mathbf{X}\) in regard to the cluster centers, based on which the compensation to the original feature embedding is computed. As shown in Figure 1, the compensation to the original embedding is subject to two branches: The first branch appends the context vector \(\mathbf{S}\) to the original embedding \(\mathbf{X}\) via concatenation, and then a multilayer perceptron (MLP) is used to transform \(\mathbf{X}\)()S into \(\Delta=MLP(\mathbf{X}\)()S), where MLP is designed to force \(\Delta\) has the same dimension as \(\mathbf{X}\), and () denotes concatenation. The second branch computes the soft gating factor \(\mathbf{W}=MLP(\mathbf{S})\) so as to obtain the weighted compensation subject to the context, that is, \(\mathbf{W}.\Delta\), where \(\mathbf{W}\) is a scalar.

In Eq. (1), we concatenate \(\mathbf{X}\) and \(\mathbf{S}\) to produce the compensation \(MLP(\mathbf{X}(\mathbf{\bar{\odot}}\mathbf{S})\) for transferring feature embedding because \(\mathbf{S}\) indicates how ambiguous \(\mathbf{X}\) is in terms of being associate with a deterministic cluster, say, context. Moreover, the soft weighting \(MLP(\mathbf{S})\) aims to control the overall compensation adaptive to the context \(\mathbf{S}\). It is apparent that anchors resulting from clustering determines the performance of the proposed solution since they are the base to figure out data distribution and aware of the contextual clue to produce compensation adaptively. Here, we make use of the algorithm referred to as Deep Embedded Clustering (DEC) (Xie et al., 2016) to obtain such anchor points, and the experiments confirm that DEC performs very well in the proposed framework.

* GTN (Zhu et al., 2018): Gated Transfer Network is used to weight features from pre-trained model, which works in the sense of feature selection.

* DELTA (Li et al., 2019): DELTA characterizes the distance between source/target networks using their outer layer outputs, and incorporates such distance as the regularization term of the loss function.

* Co-Tuning (You et al., 2020): The pre-trained model is fine-tuned using ground truth labels and probabilistic labels with learned relationship between source categories and target categories.

For MIT Indoor-67 data, the target is scene classification, which differs much from the source domain, say, object recognition.

Note that we annotate \(L^{2}\), \(L^{2}\)-SP and DELTA with * in Table 1 as they use Places 365 (Zhou et al., 2017) as source-domain data for pre-training, which is also a scene classification data. As a result, their performance seems more promising since the source domain and the target domain are focused on the same visual recognition task.

Here, the color of each pixel corresponds with the corresponding soft gating factor \(\mathbf{W}\), and a higher heat value of \(\mathbf{W}\) means a bigger compensation applied to transfer the feature embedding. We can see that bigger compensations are mostly applied to the data points locating in a self-contained region. This coincides with the principle that transfer learning is in general focused on such data examples that are incompatible with the target domain. Figure 2(b) shows the 2-dimensional histogram of maximum cosine similarity against soft gating factor \(\mathbf{W}\), which shows such a trend that lower similarity corresponds with in general higher compensation level, say, higher soft gating. Since the maximum similarity to cluster centers indicates the membership degree of the data example in association with a deterministic cluster, and a smaller value means a higher ambiguous case in terms of membership association, it is thus natural to result in higher-level transferring for feature compensation. This fully exhibits the motivation of this study, that is, why we make use of clustering centers as the anchors to figure out the contextual clue in directing feature compensation.

We propose a new transfer learning method with context-aware compensation to feature embedding, where a contextual descriptor figuring out how close from the input to each cluster center is applied, and soft gating is used to make the feature transfer focused on the data samples that truly need transferring. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline Backbone & Method & Caltech 256-30 & Caltech 256-60 & CUB-200 & Stanford Dogs & MIT Indoor-67 \\ \hline \multirow{6}{*}{ResNet101} & TransTailor & 85.3 & 87.3 & 80.7 & 91 & 78.2 \\  & PrtR & 84.5 & 87.2 & - & - & 79.2 \\  & \(L^{2}\) & 81.5±0.2 & 85.3±0.2 & - & 81.4±0.2 & 79.6±0.5* \\  & \(L^{2}\)-SP & 83.5±0.1 & 86.4±0.2 & - & 85.1±0.2 & 84.2±0.3* \\  & DELTA & 86.6 & **88.7** & 80.5 & 88.7 & 85.5* \\  & Fine tune & 82.65 & 85.91 & 83.7 & 91.17 & **80.82** \\  & Ours (I) & 83.43 & 86.36 & 83.91 & **92.24** & 80.22 \\  & Ours (II) & 83.02 & 85.27 & **84.89** & 91.75 & 80.52 \\  & Ours (I+) & **86.62** & 86.66 & - & - & - \\ \hline \multirow{6}{*}{ResNet50} & PrtR & **83.9** & **87.1** & 81.9 & - & 77.9 \\  & AdaFilter & 80.62 & 84.31 & - & 82.44 & 77.53 \\  & GTN & - & - & 79.95 & - & 77.77 \\  & \(L^{2}\)-SP & - & - & 78.44 & 86.72 & - \\  & DELTA & - & - & 78.63 & 86.01 & - \\  & BSS & - & - & 78.85 & 87.18 & - \\  & StochNorm & - & - & 79.58 & - & - \\  & Co-Tuning & - & - & 81.24 & - & - \\  & Fine tune & 79.94 & 83.04 & 83.83 & 89.79 & **79.55** \\  & Ours (I) & 80.58 & 83.39 & **83.98** & 90.5 & 79.4 \\  & Ours (II) & 81.09 & 83.73 & 83.93 & **90.92** & 79.62 \\  & Ours (I+) & 83.32 & 83.96 & - & - & - \\ \hline \multirow{6}{*}{VGG16} & TransTailor & **76.4** & **81.8** & 79.2 & **84.2** & **76.5** \\  & PrtR & - & - & 75.1 & - & 73 \\ \cline{1-1}  & GTN & - & - & 72.14 & - & 71.19 \\ \cline{1-1}  & Fine tune & 54.06 & 59.14 & 76.57 & 75.5 & 72.91 \\ \cline{1-1}  & Ours (I) & 52.61 & 58.47 & 76.51 & 75.81 & 72.23 \\ \cline{1-1}  & Ours (II) & 60.46 & 66.42 & **80.3** & 77.65 & 75.59 \\ \cline{1-1}  & Ours (III) & 66.46 & 70.66 & 64.84 & 83.39 & 71.34 \\ \cline{1-1}  & Ours (I+) & 63.37 & 66.28 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification precision (%) compared with baselines on 5 data sets

Input: Table 1: Classification precision (%) compared with baselines on 5 data sets

Input: Figure 2: (a) Visualization of the data distribution of Stanford Dogs data via PCA. The heat value of each pixel corresponds with the value of the soft gating factor W in association with this pixel. (b) 2-dimensional histogram of maximum cosine similarity against soft gating factor (W ).

Input: Figure 1: Context-directed Transfer Learning

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I assess that it is moderately overstated. While the evidence shows that clustering and similarity computations are used to determine feature compensation, several key issues exist:

1. As noted in the review, the clustering is performed only on target data, not considering source domain distribution, making it unclear how it truly measures source-target mismatch.

2. The empirical results, particularly poor performance on MIT Indoor-67, suggest limitations in handling significant domain differences.

3. While Figure 2 shows correlations between similarities and compensation levels, this is more descriptive than conclusive proof of effective domain mismatch compensation.

4. The lack of theoretical analysis makes it difficult to verify if the context truly indicates domain mismatch versus other data characteristics.

<score>4</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

This acts as the context to indicate whether and how much feature compensation is needed for the input to compensate for the mismatch between source domain and target domain.

The review comment to be evaluated is:

Strengths:
- The paper tackles an important problem of identifying the mismatch between a source domain and a target domain before transfer.
- The paper is, in general, well-aligned with literature in the area.

Weaknesses:
- The paper does not provide explanations / intuitions to most technical decisions. For instance, the proposed method is very quickly explained in roughly half a page (sections 3.1 - 3.2). For example, why is it reasonable to use DEC? Why is compensation necessary - what are the underlying factors that make it necessary once the closest cluster centers have been identified? Why is cosine similarity, and no other measure, applicable in this case?
- It is very unclear to me how the proposed method is actually helping to identify the mismatch between a source domain and a target domain. In several parts of the paper, it is mentioned that the cluster centers are calculated on the full training set, and according to my understanding it is the whole training set of the target domain for which cluster centers are being calculated? I think it would greatly benefit the explanation of the method if it would clearly distinguish which parts of it come from the source domain and which from the target domain.
- In the experiments, I do not see any reasoning as to what the proposed method works in some cases but not in others. In which cases doing this kind of clustering + compensation approach would work better? This kind of description is very important to really see the contribution of the proposed method to the problem of transfer learning.

The evidence to be evaluated is:**Transfer learning** aims to transfer the learnt knowledge from source domain to target domain (Thrun, 1995) so as to allow pre-trained models reused with minimum effort and good transferability.

Mapping-based deep transfer learning maps the source domain and target domain to a new space, in which they could be coherent to each other to some extent, and model is trained in the new space.

Domain Adversarial Neural Network (DANN) (Ajakan et al., 2014) introduces the idea of GAN to transfer learning by applying a domain classifier to minimize the difference between source and target domain in feature learning, that is, optimization on transferred feature representation is driven by not only classification but also removing the gap between source and target domain.

Fine tune is limited in that the difference between source domain and target domain is not taken into account. In view of that, \(L^{2}\)-SP (Xuhong et al., 2018) and DELTA (Li et al., 2019) apply regularization to loss function in order to compensate for such difference. GTN (Zhu et al., 2018) holds in terms of feature selection to weight features with gating neural network such that the common features shared by source domain and target domain can receive more attention.

From another point of view, parameter modification on the original network (Xuhong et al., 2018; Li et al., 2019; Zhu et al., 2018; Zhong & Maki, 2020) is not able to tackle all mismatches between source and target domain. Hereafter, AdaFilter (Guo et al., 2020) and TransTailor (Liu et al., 2021) alleviate this by reorganizing the network architecture.

In contrast to (Guo et al., 2020; Liu et al., 2021), our method can approach this goal since we just add a tiny network to obtain enhanced feature to compensate for the feature from backbone network.

Therefore, we first perform clustering on training data to obtain the centers representing data distribution. Then, we compute the similarity between the input data example and each of the centroids resulting from clustering so as to figure out the position of the input in reference to the cluster centers, where the cluster centers act as anchors to figure out data distribution as well as how far from the input to each anchor. We denote the \(K\) cluster centroids as \(\mathbf{C}=(\mathbf{C}_{1},\mathbf{C}_{2},\ldots,\mathbf{C}_{K})\) and the input as \(\mathbf{X}\). Then, we compute the cosine similarity between \(\mathbf{X}\) and \(\mathbf{C}_{1},\mathbf{C}_{2},\ldots,\mathbf{C}_{K}\) respectively, and the corresponding similarity is denoted as \(\mathbf{S}=(\mathbf{S}_{1},\mathbf{S}_{2},\ldots,\mathbf{S}_{K})\). Note that \(\mathbf{X}\) should undergo Auto-Encoder based representation learning prior to clustering and similarity computing, as required by the clustering module applied in this study, namely, Deep Embedded Clustering (DEC) (Xie et al., 2016). These similarities are referred to as context since they figure out the relative position of \(\mathbf{X}\) in regard to the cluster centers, based on which the compensation to the original feature embedding is computed. As shown in Figure 1, the compensation to the original embedding is subject to two branches: The first branch appends the context vector \(\mathbf{S}\) to the original embedding \(\mathbf{X}\) via concatenation, and then a multilayer perceptron (MLP) is used to transform \(\mathbf{X}\)()S into \(\Delta=MLP(\mathbf{X}\)()S), where MLP is designed to force \(\Delta\) has the same dimension as \(\mathbf{X}\), and () denotes concatenation. The second branch computes the soft gating factor \(\mathbf{W}=MLP(\mathbf{S})\) so as to obtain the weighted compensation subject to the context, that is, \(\mathbf{W}.\Delta\), where \(\mathbf{W}\) is a scalar.

In Eq. (1), we concatenate \(\mathbf{X}\) and \(\mathbf{S}\) to produce the compensation \(MLP(\mathbf{X}(\mathbf{\bar{\odot}}\mathbf{S})\) for transferring feature embedding because \(\mathbf{S}\) indicates how ambiguous \(\mathbf{X}\) is in terms of being associate with a deterministic cluster, say, context. Moreover, the soft weighting \(MLP(\mathbf{S})\) aims to control the overall compensation adaptive to the context \(\mathbf{S}\). It is apparent that anchors resulting from clustering determines the performance of the proposed solution since they are the base to figure out data distribution and aware of the contextual clue to produce compensation adaptively. Here, we make use of the algorithm referred to as Deep Embedded Clustering (DEC) (Xie et al., 2016) to obtain such anchor points, and the experiments confirm that DEC performs very well in the proposed framework.

* GTN (Zhu et al., 2018): Gated Transfer Network is used to weight features from pre-trained model, which works in the sense of feature selection.

* DELTA (Li et al., 2019): DELTA characterizes the distance between source/target networks using their outer layer outputs, and incorporates such distance as the regularization term of the loss function.

* Co-Tuning (You et al., 2020): The pre-trained model is fine-tuned using ground truth labels and probabilistic labels with learned relationship between source categories and target categories.

For MIT Indoor-67 data, the target is scene classification, which differs much from the source domain, say, object recognition.

Note that we annotate \(L^{2}\), \(L^{2}\)-SP and DELTA with * in Table 1 as they use Places 365 (Zhou et al., 2017) as source-domain data for pre-training, which is also a scene classification data. As a result, their performance seems more promising since the source domain and the target domain are focused on the same visual recognition task.

Here, the color of each pixel corresponds with the corresponding soft gating factor \(\mathbf{W}\), and a higher heat value of \(\mathbf{W}\) means a bigger compensation applied to transfer the feature embedding. We can see that bigger compensations are mostly applied to the data points locating in a self-contained region. This coincides with the principle that transfer learning is in general focused on such data examples that are incompatible with the target domain. Figure 2(b) shows the 2-dimensional histogram of maximum cosine similarity against soft gating factor \(\mathbf{W}\), which shows such a trend that lower similarity corresponds with in general higher compensation level, say, higher soft gating. Since the maximum similarity to cluster centers indicates the membership degree of the data example in association with a deterministic cluster, and a smaller value means a higher ambiguous case in terms of membership association, it is thus natural to result in higher-level transferring for feature compensation. This fully exhibits the motivation of this study, that is, why we make use of clustering centers as the anchors to figure out the contextual clue in directing feature compensation.

We propose a new transfer learning method with context-aware compensation to feature embedding, where a contextual descriptor figuring out how close from the input to each cluster center is applied, and soft gating is used to make the feature transfer focused on the data samples that truly need transferring. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline Backbone & Method & Caltech 256-30 & Caltech 256-60 & CUB-200 & Stanford Dogs & MIT Indoor-67 \\ \hline \multirow{6}{*}{ResNet101} & TransTailor & 85.3 & 87.3 & 80.7 & 91 & 78.2 \\  & PrtR & 84.5 & 87.2 & - & - & 79.2 \\  & \(L^{2}\) & 81.5±0.2 & 85.3±0.2 & - & 81.4±0.2 & 79.6±0.5* \\  & \(L^{2}\)-SP & 83.5±0.1 & 86.4±0.2 & - & 85.1±0.2 & 84.2±0.3* \\  & DELTA & 86.6 & **88.7** & 80.5 & 88.7 & 85.5* \\  & Fine tune & 82.65 & 85.91 & 83.7 & 91.17 & **80.82** \\  & Ours (I) & 83.43 & 86.36 & 83.91 & **92.24** & 80.22 \\  & Ours (II) & 83.02 & 85.27 & **84.89** & 91.75 & 80.52 \\  & Ours (I+) & **86.62** & 86.66 & - & - & - \\ \hline \multirow{6}{*}{ResNet50} & PrtR & **83.9** & **87.1** & 81.9 & - & 77.9 \\  & AdaFilter & 80.62 & 84.31 & - & 82.44 & 77.53 \\  & GTN & - & - & 79.95 & - & 77.77 \\  & \(L^{2}\)-SP & - & - & 78.44 & 86.72 & - \\  & DELTA & - & - & 78.63 & 86.01 & - \\  & BSS & - & - & 78.85 & 87.18 & - \\  & StochNorm & - & - & 79.58 & - & - \\  & Co-Tuning & - & - & 81.24 & - & - \\  & Fine tune & 79.94 & 83.04 & 83.83 & 89.79 & **79.55** \\  & Ours (I) & 80.58 & 83.39 & **83.98** & 90.5 & 79.4 \\  & Ours (II) & 81.09 & 83.73 & 83.93 & **90.92** & 79.62 \\  & Ours (I+) & 83.32 & 83.96 & - & - & - \\ \hline \multirow{6}{*}{VGG16} & TransTailor & **76.4** & **81.8** & 79.2 & **84.2** & **76.5** \\  & PrtR & - & - & 75.1 & - & 73 \\ \cline{1-1}  & GTN & - & - & 72.14 & - & 71.19 \\ \cline{1-1}  & Fine tune & 54.06 & 59.14 & 76.57 & 75.5 & 72.91 \\ \cline{1-1}  & Ours (I) & 52.61 & 58.47 & 76.51 & 75.81 & 72.23 \\ \cline{1-1}  & Ours (II) & 60.46 & 66.42 & **80.3** & 77.65 & 75.59 \\ \cline{1-1}  & Ours (III) & 66.46 & 70.66 & 64.84 & 83.39 & 71.34 \\ \cline{1-1}  & Ours (I+) & 63.37 & 66.28 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification precision (%) compared with baselines on 5 data sets

Input: Table 1: Classification precision (%) compared with baselines on 5 data sets

Input: Figure 2: (a) Visualization of the data distribution of Stanford Dogs data via PCA. The heat value of each pixel corresponds with the value of the soft gating factor W in association with this pixel. (b) 2-dimensional histogram of maximum cosine similarity against soft gating factor (W ).

Input: Figure 1: Context-directed Transfer Learning

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find this claim to be moderately overstated based on several factors:

1. While the evidence shows that clustering and similarity measures are used to determine feature compensation, the actual "context" role is not clearly demonstrated in the experimental results or theoretical justification, as noted in the reviewer comments.

2. The reviewer points out that the paper lacks clear explanations for why DEC clustering and cosine similarity were chosen, and how exactly the method identifies source-target domain mismatch.

3. The heat map visualization and histogram provide some empirical evidence of compensation patterns, but don't fully validate that this represents meaningful "context" for domain adaptation.

4. The experimental results show competitive but not consistently superior performance, suggesting the claimed contextual mechanism may not be as decisive as implied.

<score>4</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

This acts as the context to indicate whether and how much feature compensation is needed for the input to compensate for the mismatch between source domain and target domain.

The review comment to be evaluated is:

### Strength

- The method is interesting. Studying the contextual relation between source and target domains is novel.
- The related work section is sufficient. So much literature is surveyed.
- Experimental datasets and comparison methods are representative.

### Weakness

- Although the idea of learning contextual relation is novel, this approach is not. It remains unclear whether clustering can model the context information, or if the results of clusters and original inputs can solve the mismatch between source and target domains in transfer learning.
- The claim that "this method only adds two MLPs to the backbone network" is not correct. As can be seen from Figure 1, there are more than two MLPs: it still has an autoencoder (DEC), which is much more complicated than MLPs.
- The method part is extremely confusing and not clear, which should be rewritten. I find figure 1 hard to understand, specifically how to compute $\mathbf{X} \textcopyright \mathbf{S}$ and $\mathbf{W} \cdot \Delta$. (I even think that should be $\cdot$ instead of $.$, since you claim it's a product.)
- The success of "compensation" heavily relies on DEC (deep embedding clustering). If DEC does not give the correct clustering results, the method will fail. How to justify this? Additionally, DEC brings extra computations, which should be highlighted in the paper.
- This method will consume a lot of training time than comparison methods, according to Sec. 3.3. We see that there is more than one step of fine-tuning, which is more complicated than existing work. Authors should compare your training and inference time in the experiments.
- In experiments, you should also list the number of training parameters of each method for a more fair study since this method introduces extra neural network modules.
- More importantly, the performance on general fine-tuning tasks of this method is NOT good. According to table 1, This approach only succeeds using Resnet-101, while it does not generate competitive results in Resnet-50 and VGG16. Hence, it is difficult to say that this approach actually works in different backbones.
- No experiments to explain why this method works. Lack of detailed analysis.
- With regards to related work, I think this paper misses two very important references on the same topic:

[1] Jang Y, Lee H, Hwang S J, et al. Learning what and where to transfer[C]//International Conference on Machine Learning. PMLR, 2019: 3030-3039.

[2] Murugesan K, Sadashivaiah V, Luss R, et al. Auto-Transfer: Learning to Route Transferrable Representations[J]. arXiv preprint arXiv:2202.01011, 2022.

The evidence to be evaluated is:**Transfer learning** aims to transfer the learnt knowledge from source domain to target domain (Thrun, 1995) so as to allow pre-trained models reused with minimum effort and good transferability.

Mapping-based deep transfer learning maps the source domain and target domain to a new space, in which they could be coherent to each other to some extent, and model is trained in the new space.

Domain Adversarial Neural Network (DANN) (Ajakan et al., 2014) introduces the idea of GAN to transfer learning by applying a domain classifier to minimize the difference between source and target domain in feature learning, that is, optimization on transferred feature representation is driven by not only classification but also removing the gap between source and target domain.

Fine tune is limited in that the difference between source domain and target domain is not taken into account. In view of that, \(L^{2}\)-SP (Xuhong et al., 2018) and DELTA (Li et al., 2019) apply regularization to loss function in order to compensate for such difference. GTN (Zhu et al., 2018) holds in terms of feature selection to weight features with gating neural network such that the common features shared by source domain and target domain can receive more attention.

From another point of view, parameter modification on the original network (Xuhong et al., 2018; Li et al., 2019; Zhu et al., 2018; Zhong & Maki, 2020) is not able to tackle all mismatches between source and target domain. Hereafter, AdaFilter (Guo et al., 2020) and TransTailor (Liu et al., 2021) alleviate this by reorganizing the network architecture.

In contrast to (Guo et al., 2020; Liu et al., 2021), our method can approach this goal since we just add a tiny network to obtain enhanced feature to compensate for the feature from backbone network.

Therefore, we first perform clustering on training data to obtain the centers representing data distribution. Then, we compute the similarity between the input data example and each of the centroids resulting from clustering so as to figure out the position of the input in reference to the cluster centers, where the cluster centers act as anchors to figure out data distribution as well as how far from the input to each anchor. We denote the \(K\) cluster centroids as \(\mathbf{C}=(\mathbf{C}_{1},\mathbf{C}_{2},\ldots,\mathbf{C}_{K})\) and the input as \(\mathbf{X}\). Then, we compute the cosine similarity between \(\mathbf{X}\) and \(\mathbf{C}_{1},\mathbf{C}_{2},\ldots,\mathbf{C}_{K}\) respectively, and the corresponding similarity is denoted as \(\mathbf{S}=(\mathbf{S}_{1},\mathbf{S}_{2},\ldots,\mathbf{S}_{K})\). Note that \(\mathbf{X}\) should undergo Auto-Encoder based representation learning prior to clustering and similarity computing, as required by the clustering module applied in this study, namely, Deep Embedded Clustering (DEC) (Xie et al., 2016). These similarities are referred to as context since they figure out the relative position of \(\mathbf{X}\) in regard to the cluster centers, based on which the compensation to the original feature embedding is computed. As shown in Figure 1, the compensation to the original embedding is subject to two branches: The first branch appends the context vector \(\mathbf{S}\) to the original embedding \(\mathbf{X}\) via concatenation, and then a multilayer perceptron (MLP) is used to transform \(\mathbf{X}\)()S into \(\Delta=MLP(\mathbf{X}\)()S), where MLP is designed to force \(\Delta\) has the same dimension as \(\mathbf{X}\), and () denotes concatenation. The second branch computes the soft gating factor \(\mathbf{W}=MLP(\mathbf{S})\) so as to obtain the weighted compensation subject to the context, that is, \(\mathbf{W}.\Delta\), where \(\mathbf{W}\) is a scalar.

In Eq. (1), we concatenate \(\mathbf{X}\) and \(\mathbf{S}\) to produce the compensation \(MLP(\mathbf{X}(\mathbf{\bar{\odot}}\mathbf{S})\) for transferring feature embedding because \(\mathbf{S}\) indicates how ambiguous \(\mathbf{X}\) is in terms of being associate with a deterministic cluster, say, context. Moreover, the soft weighting \(MLP(\mathbf{S})\) aims to control the overall compensation adaptive to the context \(\mathbf{S}\). It is apparent that anchors resulting from clustering determines the performance of the proposed solution since they are the base to figure out data distribution and aware of the contextual clue to produce compensation adaptively. Here, we make use of the algorithm referred to as Deep Embedded Clustering (DEC) (Xie et al., 2016) to obtain such anchor points, and the experiments confirm that DEC performs very well in the proposed framework.

* GTN (Zhu et al., 2018): Gated Transfer Network is used to weight features from pre-trained model, which works in the sense of feature selection.

* DELTA (Li et al., 2019): DELTA characterizes the distance between source/target networks using their outer layer outputs, and incorporates such distance as the regularization term of the loss function.

* Co-Tuning (You et al., 2020): The pre-trained model is fine-tuned using ground truth labels and probabilistic labels with learned relationship between source categories and target categories.

For MIT Indoor-67 data, the target is scene classification, which differs much from the source domain, say, object recognition.

Note that we annotate \(L^{2}\), \(L^{2}\)-SP and DELTA with * in Table 1 as they use Places 365 (Zhou et al., 2017) as source-domain data for pre-training, which is also a scene classification data. As a result, their performance seems more promising since the source domain and the target domain are focused on the same visual recognition task.

Here, the color of each pixel corresponds with the corresponding soft gating factor \(\mathbf{W}\), and a higher heat value of \(\mathbf{W}\) means a bigger compensation applied to transfer the feature embedding. We can see that bigger compensations are mostly applied to the data points locating in a self-contained region. This coincides with the principle that transfer learning is in general focused on such data examples that are incompatible with the target domain. Figure 2(b) shows the 2-dimensional histogram of maximum cosine similarity against soft gating factor \(\mathbf{W}\), which shows such a trend that lower similarity corresponds with in general higher compensation level, say, higher soft gating. Since the maximum similarity to cluster centers indicates the membership degree of the data example in association with a deterministic cluster, and a smaller value means a higher ambiguous case in terms of membership association, it is thus natural to result in higher-level transferring for feature compensation. This fully exhibits the motivation of this study, that is, why we make use of clustering centers as the anchors to figure out the contextual clue in directing feature compensation.

We propose a new transfer learning method with context-aware compensation to feature embedding, where a contextual descriptor figuring out how close from the input to each cluster center is applied, and soft gating is used to make the feature transfer focused on the data samples that truly need transferring. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline Backbone & Method & Caltech 256-30 & Caltech 256-60 & CUB-200 & Stanford Dogs & MIT Indoor-67 \\ \hline \multirow{6}{*}{ResNet101} & TransTailor & 85.3 & 87.3 & 80.7 & 91 & 78.2 \\  & PrtR & 84.5 & 87.2 & - & - & 79.2 \\  & \(L^{2}\) & 81.5±0.2 & 85.3±0.2 & - & 81.4±0.2 & 79.6±0.5* \\  & \(L^{2}\)-SP & 83.5±0.1 & 86.4±0.2 & - & 85.1±0.2 & 84.2±0.3* \\  & DELTA & 86.6 & **88.7** & 80.5 & 88.7 & 85.5* \\  & Fine tune & 82.65 & 85.91 & 83.7 & 91.17 & **80.82** \\  & Ours (I) & 83.43 & 86.36 & 83.91 & **92.24** & 80.22 \\  & Ours (II) & 83.02 & 85.27 & **84.89** & 91.75 & 80.52 \\  & Ours (I+) & **86.62** & 86.66 & - & - & - \\ \hline \multirow{6}{*}{ResNet50} & PrtR & **83.9** & **87.1** & 81.9 & - & 77.9 \\  & AdaFilter & 80.62 & 84.31 & - & 82.44 & 77.53 \\  & GTN & - & - & 79.95 & - & 77.77 \\  & \(L^{2}\)-SP & - & - & 78.44 & 86.72 & - \\  & DELTA & - & - & 78.63 & 86.01 & - \\  & BSS & - & - & 78.85 & 87.18 & - \\  & StochNorm & - & - & 79.58 & - & - \\  & Co-Tuning & - & - & 81.24 & - & - \\  & Fine tune & 79.94 & 83.04 & 83.83 & 89.79 & **79.55** \\  & Ours (I) & 80.58 & 83.39 & **83.98** & 90.5 & 79.4 \\  & Ours (II) & 81.09 & 83.73 & 83.93 & **90.92** & 79.62 \\  & Ours (I+) & 83.32 & 83.96 & - & - & - \\ \hline \multirow{6}{*}{VGG16} & TransTailor & **76.4** & **81.8** & 79.2 & **84.2** & **76.5** \\  & PrtR & - & - & 75.1 & - & 73 \\ \cline{1-1}  & GTN & - & - & 72.14 & - & 71.19 \\ \cline{1-1}  & Fine tune & 54.06 & 59.14 & 76.57 & 75.5 & 72.91 \\ \cline{1-1}  & Ours (I) & 52.61 & 58.47 & 76.51 & 75.81 & 72.23 \\ \cline{1-1}  & Ours (II) & 60.46 & 66.42 & **80.3** & 77.65 & 75.59 \\ \cline{1-1}  & Ours (III) & 66.46 & 70.66 & 64.84 & 83.39 & 71.34 \\ \cline{1-1}  & Ours (I+) & 63.37 & 66.28 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification precision (%) compared with baselines on 5 data sets

Input: Table 1: Classification precision (%) compared with baselines on 5 data sets

Input: Figure 2: (a) Visualization of the data distribution of Stanford Dogs data via PCA. The heat value of each pixel corresponds with the value of the soft gating factor W in association with this pixel. (b) 2-dimensional histogram of maximum cosine similarity against soft gating factor (W ).

Input: Figure 1: Context-directed Transfer Learning

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After careful review of the claim, evidence, and reviewer comments, I find the claim to be somewhat overstated.

While the evidence shows that the method does use clustering and similarity measures to determine feature compensation, several key issues emerge:

1. The reviewer correctly points out that clustering's effectiveness in modeling context information is not clearly demonstrated
2. The results in Table 1 show inconsistent performance across different backbones, particularly underperforming with VGG16
3. There's limited analysis explaining why/how the contextual relationship actually helps with domain adaptation
4. The reviewer notes the heavy reliance on DEC for success, which isn't adequately justified

While the method does provide some context through clustering and similarity measures, claiming this definitively indicates "whether and how much feature compensation is needed" appears to overstate what's demonstrated by the evidence.

<score>4</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

This acts as the context to indicate whether and how much feature compensation is needed for the input to compensate for the mismatch between source domain and target domain.

The review comment to be evaluated is:

Strengths:
the paper is well written, methodology is well-described, and the problem addressed is relevant.

Weaknesses.
The author is describing how to use data centers to do data compensation, however, how this is related to data transferring is poorly described. For examples, is data centers computed on target data set? how is its feature vector computed, using the pretrained network? using distance to get compensation factor seems only to be working under a strong assumption that this is used for classification, and it only works for image classification tasks? why using distance to centers work? all these questions are not answered properly.

Based on the experimental setup, it is just finetuning the pretrained network using some downstream tasks. It just barely competes with SOTA methods, seems that with or without such 2 layers of compensation networks does not change the result at all.

The evidence to be evaluated is:**Transfer learning** aims to transfer the learnt knowledge from source domain to target domain (Thrun, 1995) so as to allow pre-trained models reused with minimum effort and good transferability.

Mapping-based deep transfer learning maps the source domain and target domain to a new space, in which they could be coherent to each other to some extent, and model is trained in the new space.

Domain Adversarial Neural Network (DANN) (Ajakan et al., 2014) introduces the idea of GAN to transfer learning by applying a domain classifier to minimize the difference between source and target domain in feature learning, that is, optimization on transferred feature representation is driven by not only classification but also removing the gap between source and target domain.

Fine tune is limited in that the difference between source domain and target domain is not taken into account. In view of that, \(L^{2}\)-SP (Xuhong et al., 2018) and DELTA (Li et al., 2019) apply regularization to loss function in order to compensate for such difference. GTN (Zhu et al., 2018) holds in terms of feature selection to weight features with gating neural network such that the common features shared by source domain and target domain can receive more attention.

From another point of view, parameter modification on the original network (Xuhong et al., 2018; Li et al., 2019; Zhu et al., 2018; Zhong & Maki, 2020) is not able to tackle all mismatches between source and target domain. Hereafter, AdaFilter (Guo et al., 2020) and TransTailor (Liu et al., 2021) alleviate this by reorganizing the network architecture.

In contrast to (Guo et al., 2020; Liu et al., 2021), our method can approach this goal since we just add a tiny network to obtain enhanced feature to compensate for the feature from backbone network.

Therefore, we first perform clustering on training data to obtain the centers representing data distribution. Then, we compute the similarity between the input data example and each of the centroids resulting from clustering so as to figure out the position of the input in reference to the cluster centers, where the cluster centers act as anchors to figure out data distribution as well as how far from the input to each anchor. We denote the \(K\) cluster centroids as \(\mathbf{C}=(\mathbf{C}_{1},\mathbf{C}_{2},\ldots,\mathbf{C}_{K})\) and the input as \(\mathbf{X}\). Then, we compute the cosine similarity between \(\mathbf{X}\) and \(\mathbf{C}_{1},\mathbf{C}_{2},\ldots,\mathbf{C}_{K}\) respectively, and the corresponding similarity is denoted as \(\mathbf{S}=(\mathbf{S}_{1},\mathbf{S}_{2},\ldots,\mathbf{S}_{K})\). Note that \(\mathbf{X}\) should undergo Auto-Encoder based representation learning prior to clustering and similarity computing, as required by the clustering module applied in this study, namely, Deep Embedded Clustering (DEC) (Xie et al., 2016). These similarities are referred to as context since they figure out the relative position of \(\mathbf{X}\) in regard to the cluster centers, based on which the compensation to the original feature embedding is computed. As shown in Figure 1, the compensation to the original embedding is subject to two branches: The first branch appends the context vector \(\mathbf{S}\) to the original embedding \(\mathbf{X}\) via concatenation, and then a multilayer perceptron (MLP) is used to transform \(\mathbf{X}\)()S into \(\Delta=MLP(\mathbf{X}\)()S), where MLP is designed to force \(\Delta\) has the same dimension as \(\mathbf{X}\), and () denotes concatenation. The second branch computes the soft gating factor \(\mathbf{W}=MLP(\mathbf{S})\) so as to obtain the weighted compensation subject to the context, that is, \(\mathbf{W}.\Delta\), where \(\mathbf{W}\) is a scalar.

In Eq. (1), we concatenate \(\mathbf{X}\) and \(\mathbf{S}\) to produce the compensation \(MLP(\mathbf{X}(\mathbf{\bar{\odot}}\mathbf{S})\) for transferring feature embedding because \(\mathbf{S}\) indicates how ambiguous \(\mathbf{X}\) is in terms of being associate with a deterministic cluster, say, context. Moreover, the soft weighting \(MLP(\mathbf{S})\) aims to control the overall compensation adaptive to the context \(\mathbf{S}\). It is apparent that anchors resulting from clustering determines the performance of the proposed solution since they are the base to figure out data distribution and aware of the contextual clue to produce compensation adaptively. Here, we make use of the algorithm referred to as Deep Embedded Clustering (DEC) (Xie et al., 2016) to obtain such anchor points, and the experiments confirm that DEC performs very well in the proposed framework.

* GTN (Zhu et al., 2018): Gated Transfer Network is used to weight features from pre-trained model, which works in the sense of feature selection.

* DELTA (Li et al., 2019): DELTA characterizes the distance between source/target networks using their outer layer outputs, and incorporates such distance as the regularization term of the loss function.

* Co-Tuning (You et al., 2020): The pre-trained model is fine-tuned using ground truth labels and probabilistic labels with learned relationship between source categories and target categories.

For MIT Indoor-67 data, the target is scene classification, which differs much from the source domain, say, object recognition.

Note that we annotate \(L^{2}\), \(L^{2}\)-SP and DELTA with * in Table 1 as they use Places 365 (Zhou et al., 2017) as source-domain data for pre-training, which is also a scene classification data. As a result, their performance seems more promising since the source domain and the target domain are focused on the same visual recognition task.

Here, the color of each pixel corresponds with the corresponding soft gating factor \(\mathbf{W}\), and a higher heat value of \(\mathbf{W}\) means a bigger compensation applied to transfer the feature embedding. We can see that bigger compensations are mostly applied to the data points locating in a self-contained region. This coincides with the principle that transfer learning is in general focused on such data examples that are incompatible with the target domain. Figure 2(b) shows the 2-dimensional histogram of maximum cosine similarity against soft gating factor \(\mathbf{W}\), which shows such a trend that lower similarity corresponds with in general higher compensation level, say, higher soft gating. Since the maximum similarity to cluster centers indicates the membership degree of the data example in association with a deterministic cluster, and a smaller value means a higher ambiguous case in terms of membership association, it is thus natural to result in higher-level transferring for feature compensation. This fully exhibits the motivation of this study, that is, why we make use of clustering centers as the anchors to figure out the contextual clue in directing feature compensation.

We propose a new transfer learning method with context-aware compensation to feature embedding, where a contextual descriptor figuring out how close from the input to each cluster center is applied, and soft gating is used to make the feature transfer focused on the data samples that truly need transferring. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline Backbone & Method & Caltech 256-30 & Caltech 256-60 & CUB-200 & Stanford Dogs & MIT Indoor-67 \\ \hline \multirow{6}{*}{ResNet101} & TransTailor & 85.3 & 87.3 & 80.7 & 91 & 78.2 \\  & PrtR & 84.5 & 87.2 & - & - & 79.2 \\  & \(L^{2}\) & 81.5±0.2 & 85.3±0.2 & - & 81.4±0.2 & 79.6±0.5* \\  & \(L^{2}\)-SP & 83.5±0.1 & 86.4±0.2 & - & 85.1±0.2 & 84.2±0.3* \\  & DELTA & 86.6 & **88.7** & 80.5 & 88.7 & 85.5* \\  & Fine tune & 82.65 & 85.91 & 83.7 & 91.17 & **80.82** \\  & Ours (I) & 83.43 & 86.36 & 83.91 & **92.24** & 80.22 \\  & Ours (II) & 83.02 & 85.27 & **84.89** & 91.75 & 80.52 \\  & Ours (I+) & **86.62** & 86.66 & - & - & - \\ \hline \multirow{6}{*}{ResNet50} & PrtR & **83.9** & **87.1** & 81.9 & - & 77.9 \\  & AdaFilter & 80.62 & 84.31 & - & 82.44 & 77.53 \\  & GTN & - & - & 79.95 & - & 77.77 \\  & \(L^{2}\)-SP & - & - & 78.44 & 86.72 & - \\  & DELTA & - & - & 78.63 & 86.01 & - \\  & BSS & - & - & 78.85 & 87.18 & - \\  & StochNorm & - & - & 79.58 & - & - \\  & Co-Tuning & - & - & 81.24 & - & - \\  & Fine tune & 79.94 & 83.04 & 83.83 & 89.79 & **79.55** \\  & Ours (I) & 80.58 & 83.39 & **83.98** & 90.5 & 79.4 \\  & Ours (II) & 81.09 & 83.73 & 83.93 & **90.92** & 79.62 \\  & Ours (I+) & 83.32 & 83.96 & - & - & - \\ \hline \multirow{6}{*}{VGG16} & TransTailor & **76.4** & **81.8** & 79.2 & **84.2** & **76.5** \\  & PrtR & - & - & 75.1 & - & 73 \\ \cline{1-1}  & GTN & - & - & 72.14 & - & 71.19 \\ \cline{1-1}  & Fine tune & 54.06 & 59.14 & 76.57 & 75.5 & 72.91 \\ \cline{1-1}  & Ours (I) & 52.61 & 58.47 & 76.51 & 75.81 & 72.23 \\ \cline{1-1}  & Ours (II) & 60.46 & 66.42 & **80.3** & 77.65 & 75.59 \\ \cline{1-1}  & Ours (III) & 66.46 & 70.66 & 64.84 & 83.39 & 71.34 \\ \cline{1-1}  & Ours (I+) & 63.37 & 66.28 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification precision (%) compared with baselines on 5 data sets

Input: Table 1: Classification precision (%) compared with baselines on 5 data sets

Input: Figure 2: (a) Visualization of the data distribution of Stanford Dogs data via PCA. The heat value of each pixel corresponds with the value of the soft gating factor W in association with this pixel. (b) 2-dimensional histogram of maximum cosine similarity against soft gating factor (W ).

Input: Figure 1: Context-directed Transfer Learning

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim, evidence, and reviewer comments, I find this claim to be moderately overstated. Here's why:

1. While the evidence shows that clustering centers and similarity metrics are used to determine feature compensation, the claim that this "acts as context" is not fully substantiated. The reviewer correctly points out that fundamental questions about how the data centers relate to transfer learning remain unanswered.

2. The experimental results in Table 1 show mixed performance - sometimes better, sometimes worse than baselines. This suggests the method's effectiveness is not as definitive as implied.

3. The evidence demonstrates a correlation between similarity values and compensation levels (Figure 2b), but doesn't conclusively prove this is the optimal or necessary approach for determining compensation needs.

4. As highlighted in the review comments, the method appears to work similarly to standard fine-tuning, raising questions about whether the added complexity truly provides meaningful benefits.

<score>4</score>