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:

Minor network-level change with two tiny components added, feature-level compensation with context awareness on whether and how much transferring is needed, and minimum effort to render a sound solution with big performance improvement;

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.

Another classical method, namely fine tune (Yosinski et al., 2014), reuses the original network without any change on the structure and just modifies the parameter values with the data from target domain, where fine tune can be performed on the whole network, or a part of them after fixing the other part.

Some methods (Xuhong et al., 2018; Li et al., 2019; Zhu et al., 2018; Zhong & Maki, 2020) advance it by introducing new loss functions and the others (Guo et al., 2020; Liu et al., 2021) achieve advance by optimizing the network structure.

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.

However, they change the network substantially, which deviates a bit from the origin of transfer learning, that is, gain better discriminant with minor change and little effort. 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.

For a given example, whether and how much the corresponding embedding should be changed to approach a new task is subject to its position in the data distribution.

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.

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. 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.

The transfer network to produce \(\mathbf{W}.\Delta=MLP(\mathbf{S}).MLP(\mathbf{X}\)()S) is trained end to end. Once \(W=0\), the overall embedding in Eq. (1) degrades to that of backbone network, and a higher \(W\) corresponds with a bigger transfer in terms of compensating the embedding.

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. The proposed transfer learning framework can work in 4 manners to form the following solutions:* (I) At first, fine tune the backbone network, and then fix the backbone and train the transfer network only.

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

* (III) Fix the backbone network and just train the transfer network.

* AdaFilter (Guo et al., 2020): Layer-wise Recurrent Gated Network is used to select the pre-trained as well as fine-tuned filters reusable to form a new pipeline.

* 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.

* PtR (Zhong & Maki, 2020): Pseudo-regression Task Loss is combined with Cross Entropy Loss as a regularization term once the training approaches a sound solution, by whichfeature learning takes into account not only the classification perspective but also the transferability of features.

* \(L^{2}\)-SP (Xuhong et al., 2018): \(L^{2}\)-SP penalty is applied to the parameters of a pre-trained model in the form of regularization so as to enforce the weights hold sparsely.

* BSS (Chen et al., 2019): Batch Spectral Shrinkage (BSS) inhibits negative transfer by suppressing the spectral components with small singular values that correspond to detrimental pre-trained knowledge.

* StochNorm (Kou et al., 2020): Stochastic Normalization (StochNorm) uses a two-branch normalization architecture to avoid model over-depending on biased statistics.

**ResNet-101:** On CUB-200 and Stanford dog data, the proposed method outperforms the baselines. For CUB-200 data, the precision improvement is about 4% compared with TransTailor, which is significant.

Consequently, the performance goes up to 86.62%, which outperforms all the baselines.

For our solution (I+), however, its performance improvement compared with that on Caltech 256-30 is minor as the full training data based clustering has lead to the best extent to figure out the data distribution, and the minor improvement comes from the backbone trained with more training data.

The result listed in Table 1 shows that our model performs best and achieves remarkable performance improvement on Caltech 256-30, CUB-200, and Stanford Dogs Data.

In an overall sense, our solution is technically sound and performs better in comparison with the state-of-the-art methods.

According to Table 2, the full model (solution II) outperforms the other settings on CUB-200 and Stanford Dogs data.

Further, Table 4 shows that the performance change is minor with different feature dimensionality of DEC Auto-Encoder.

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. The experiments demonstrate that the proposed feature transferring scheme is technically sound, and a big performance improvement is achieved using a tiny network.

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

 \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline W= & Clustering & \(\Delta\)= & Caltech 256-30 & Caltech 256-60 & CUB-200 & Stanford Dogs & MIT Indoor-67 \\ \hline \hline MLP(S) & DEC & MLP(X\(\otimes\)S) & 83.02 & 85.27 & 84.89 & **91.75** & 80.52 \\ \hline
1 & DEC & MLP(X\(\otimes\)S) & 82.96 & 85.44 & 84.6 & 91.5 & **81.11** \\ MLP(S) & Random & MLP(X\(\otimes\)S) & 83.49 & **86.05** & 84.53 & 91.41 & 80.89 \\ MLP(S) & K-Means & MLP(X\(\otimes\)S) & **84.04** & 85.93 & **85.07** & 91.39 & 80.29 \\ MLP(S) & DEC & MLP(X) & 83.24 & 85.78 & 84.58 & 91.63 & 80.82 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification precision (%) under different settingsembedding of every data sample should be transferred, where W=1 leads to the best performance. Since this data set is developed for scene classification with layout not fixed between objects, this causes the target domain totally different from the source domain. In such a case, every data example undergoing transfer learning is sound. For the Caltech 256 data, 30/60 data samples per class are randomly selected for training. Since so few data are too sparse to figure out fully the actual distribution of the data, it is impossible for any clustering algorithm to capture the truly representative centers. In such cases, randomly selected representatives even work better for Caltech 256-60 data as shown in Table 2. For Caltech 256-30 and CUB-200 data, clustering with K-Means leads to the best performance while DEC performs best on the other two data sets. This means that clustering works in our framework but which one is more suitable merits further studies. Yet, DEC fits the end-to-end learning well due to its neural network nature.

 \begin{table}
\begin{tabular}{c|c c c c c c} \hline \hline Dim & 100 & 200 & 300 & 400 & 500 & 600 & 700 \\ \hline \% & **84.89** & 84.32 & 84.27 & 84.19 & 84.24 & 84.41 & 84.79 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification precision against feature dimensionality arising from DEC Auto-Encoder on CUB-200 data

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

Input: Table 2: Classification precision (%) under different settings

Input: Table 4: Classification precision against feature dimensionality arising from DEC Auto-Encoder on CUB-200 data

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:
Based on the review comments and evidence, the claim appears to be significantly overstated. Here's why:

1. The claim of "minor network-level change with two tiny components" is contradicted by the evidence showing it includes an autoencoder (DEC) and multiple MLPs, making the architecture more complex than stated.

2. The performance improvements are inconsistent across different backbones - while showing good results with ResNet-101, it underperforms with ResNet-50 and VGG16.

3. The "minimum effort" claim is contradicted by evidence showing multiple fine-tuning steps and longer training times compared to existing methods.

4. The "big performance improvement" is only selectively true for certain configurations and datasets.

<score>5</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:

Minor network-level change with two tiny components added, feature-level compensation with context awareness on whether and how much transferring is needed, and minimum effort to render a sound solution with big performance improvement;

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.

Another classical method, namely fine tune (Yosinski et al., 2014), reuses the original network without any change on the structure and just modifies the parameter values with the data from target domain, where fine tune can be performed on the whole network, or a part of them after fixing the other part.

Some methods (Xuhong et al., 2018; Li et al., 2019; Zhu et al., 2018; Zhong & Maki, 2020) advance it by introducing new loss functions and the others (Guo et al., 2020; Liu et al., 2021) achieve advance by optimizing the network structure.

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.

However, they change the network substantially, which deviates a bit from the origin of transfer learning, that is, gain better discriminant with minor change and little effort. 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.

For a given example, whether and how much the corresponding embedding should be changed to approach a new task is subject to its position in the data distribution.

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.

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. 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.

The transfer network to produce \(\mathbf{W}.\Delta=MLP(\mathbf{S}).MLP(\mathbf{X}\)()S) is trained end to end. Once \(W=0\), the overall embedding in Eq. (1) degrades to that of backbone network, and a higher \(W\) corresponds with a bigger transfer in terms of compensating the embedding.

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. The proposed transfer learning framework can work in 4 manners to form the following solutions:* (I) At first, fine tune the backbone network, and then fix the backbone and train the transfer network only.

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

* (III) Fix the backbone network and just train the transfer network.

* AdaFilter (Guo et al., 2020): Layer-wise Recurrent Gated Network is used to select the pre-trained as well as fine-tuned filters reusable to form a new pipeline.

* 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.

* PtR (Zhong & Maki, 2020): Pseudo-regression Task Loss is combined with Cross Entropy Loss as a regularization term once the training approaches a sound solution, by whichfeature learning takes into account not only the classification perspective but also the transferability of features.

* \(L^{2}\)-SP (Xuhong et al., 2018): \(L^{2}\)-SP penalty is applied to the parameters of a pre-trained model in the form of regularization so as to enforce the weights hold sparsely.

* BSS (Chen et al., 2019): Batch Spectral Shrinkage (BSS) inhibits negative transfer by suppressing the spectral components with small singular values that correspond to detrimental pre-trained knowledge.

* StochNorm (Kou et al., 2020): Stochastic Normalization (StochNorm) uses a two-branch normalization architecture to avoid model over-depending on biased statistics.

**ResNet-101:** On CUB-200 and Stanford dog data, the proposed method outperforms the baselines. For CUB-200 data, the precision improvement is about 4% compared with TransTailor, which is significant.

Consequently, the performance goes up to 86.62%, which outperforms all the baselines.

For our solution (I+), however, its performance improvement compared with that on Caltech 256-30 is minor as the full training data based clustering has lead to the best extent to figure out the data distribution, and the minor improvement comes from the backbone trained with more training data.

The result listed in Table 1 shows that our model performs best and achieves remarkable performance improvement on Caltech 256-30, CUB-200, and Stanford Dogs Data.

In an overall sense, our solution is technically sound and performs better in comparison with the state-of-the-art methods.

According to Table 2, the full model (solution II) outperforms the other settings on CUB-200 and Stanford Dogs data.

Further, Table 4 shows that the performance change is minor with different feature dimensionality of DEC Auto-Encoder.

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. The experiments demonstrate that the proposed feature transferring scheme is technically sound, and a big performance improvement is achieved using a tiny network.

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

 \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline W= & Clustering & \(\Delta\)= & Caltech 256-30 & Caltech 256-60 & CUB-200 & Stanford Dogs & MIT Indoor-67 \\ \hline \hline MLP(S) & DEC & MLP(X\(\otimes\)S) & 83.02 & 85.27 & 84.89 & **91.75** & 80.52 \\ \hline
1 & DEC & MLP(X\(\otimes\)S) & 82.96 & 85.44 & 84.6 & 91.5 & **81.11** \\ MLP(S) & Random & MLP(X\(\otimes\)S) & 83.49 & **86.05** & 84.53 & 91.41 & 80.89 \\ MLP(S) & K-Means & MLP(X\(\otimes\)S) & **84.04** & 85.93 & **85.07** & 91.39 & 80.29 \\ MLP(S) & DEC & MLP(X) & 83.24 & 85.78 & 84.58 & 91.63 & 80.82 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification precision (%) under different settingsembedding of every data sample should be transferred, where W=1 leads to the best performance. Since this data set is developed for scene classification with layout not fixed between objects, this causes the target domain totally different from the source domain. In such a case, every data example undergoing transfer learning is sound. For the Caltech 256 data, 30/60 data samples per class are randomly selected for training. Since so few data are too sparse to figure out fully the actual distribution of the data, it is impossible for any clustering algorithm to capture the truly representative centers. In such cases, randomly selected representatives even work better for Caltech 256-60 data as shown in Table 2. For Caltech 256-30 and CUB-200 data, clustering with K-Means leads to the best performance while DEC performs best on the other two data sets. This means that clustering works in our framework but which one is more suitable merits further studies. Yet, DEC fits the end-to-end learning well due to its neural network nature.

 \begin{table}
\begin{tabular}{c|c c c c c c} \hline \hline Dim & 100 & 200 & 300 & 400 & 500 & 600 & 700 \\ \hline \% & **84.89** & 84.32 & 84.27 & 84.19 & 84.24 & 84.41 & 84.79 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification precision against feature dimensionality arising from DEC Auto-Encoder on CUB-200 data

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

Input: Table 2: Classification precision (%) under different settings

Input: Table 4: Classification precision against feature dimensionality arising from DEC Auto-Encoder on CUB-200 data

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 evidence and reviewers' comments, I find the claim to be moderately overstated. Here's why:

1. While the network changes are indeed minimal (two small components), the performance improvements are not consistently "big" across all datasets and models. Tables 1 and 2 show mixed results - sometimes performing better, sometimes worse than baselines.

2. The reviewer points out that the method "barely competes with SOTA methods" and questions whether the compensation networks make meaningful differences.

3. The "feature-level compensation with context awareness" works under specific assumptions and may be limited to classification tasks, as noted in the review comments.

4. The experimental evidence doesn't fully validate that this is achieved with "minimum effort" as claimed, since multiple training configurations (I, II, III, I+) were needed.

<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:

The concept of context based on self-positioning in reference to the anchors (cluster centers) is proposed for figuring out the demanding degree in transferring the representation of each given example.

The evidence to be evaluated is: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. Compared with the Pseudo label used in (Zhong & Maki, 2020), we use the similarity between the data example of interest to all the centers from clustering, which figures out the contextual situation regarding feature discriminant more comprehensively than a simple Pseudo label.

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 \(\mathbf{S}=(\mathbf{S}_{1},\mathbf{S}_{2},\ldots,\mathbf{S}_{K})\).

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.

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.

For our solution, the number of cluster centers is set to the true class number of the corresponding data set, which is 256, 200, 120, and 67 for Caltech 256, CUB-200, Stanford Dogs, and MIT Indoor-67, respectively.

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.

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.

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 that the claim about using context based on self-positioning relative to anchors (cluster centers) is well-supported by the detailed evidence provided. The paper clearly demonstrates:
1. The technical implementation using cluster centers as anchors
2. The mathematical formulation of similarity calculations
3. Visual evidence through PCA visualization and histograms
4. Experimental validation across multiple datasets
5. Clear explanation of how the contextual information guides feature compensation

The claim accurately reflects the methodology without overstating its capabilities or implications. The evidence thoroughly documents how the context is derived from cluster center relationships and used to determine transfer requirements.

<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:

The concept of context based on self-positioning in reference to the anchors (cluster centers) is proposed for figuring out the demanding degree in transferring the representation of each given example.

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: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. Compared with the Pseudo label used in (Zhong & Maki, 2020), we use the similarity between the data example of interest to all the centers from clustering, which figures out the contextual situation regarding feature discriminant more comprehensively than a simple Pseudo label.

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 \(\mathbf{S}=(\mathbf{S}_{1},\mathbf{S}_{2},\ldots,\mathbf{S}_{K})\).

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.

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.

For our solution, the number of cluster centers is set to the true class number of the corresponding data set, which is 256, 200, 120, and 67 for Caltech 256, CUB-200, Stanford Dogs, and MIT Indoor-67, respectively.

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.

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.

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. While the evidence shows that the authors do use cluster centers as anchors and compute similarities to determine contextual positioning, there are significant concerns raised in the review about whether this actually achieves what is claimed regarding "figuring out the demanding degree in transferring." The reviewer points out that since clustering is performed only on target data, the similarities don't truly indicate how far an example is from the source distribution, making it questionable whether this actually determines transfer needs effectively. The evidence shows the technical implementation but doesn't fully validate the conceptual claim about transfer demands.

<score>4</score>

Justification:
1. The reviewer raises a valid fundamental concern that the method may not actually measure "how much transferring is needed" since it only looks at target domain clustering
2. The evidence shows technical implementation details but lacks theoretical validation of the core claim
3. While the paper demonstrates the clustering and similarity computation approach, it doesn't conclusively prove this determines transfer demands effectively
4. The experimental results are described as "not particularly convincing" by the reviewer
5. The claim states this concept is "proposed for figuring out" rather than proven to determine transfer demands, but still overstates what the evidence fully supports


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:

The concept of context based on self-positioning in reference to the anchors (cluster centers) is proposed for figuring out the demanding degree in transferring the representation of each given example.

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: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. Compared with the Pseudo label used in (Zhong & Maki, 2020), we use the similarity between the data example of interest to all the centers from clustering, which figures out the contextual situation regarding feature discriminant more comprehensively than a simple Pseudo label.

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 \(\mathbf{S}=(\mathbf{S}_{1},\mathbf{S}_{2},\ldots,\mathbf{S}_{K})\).

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.

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.

For our solution, the number of cluster centers is set to the true class number of the corresponding data set, which is 256, 200, 120, and 67 for Caltech 256, CUB-200, Stanford Dogs, and MIT Indoor-67, respectively.

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.

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.

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 review comments, I'll evaluate whether it is well-stated or overstated.

Summary: The claim about using context based on self-positioning relative to cluster centers for determining transfer demands is moderately overstated. While the evidence shows a detailed technical implementation using cluster centers and similarity measures, several key concerns raised in the review comments point to gaps:
- Lack of clear explanations for technical decisions (why DEC, why cosine similarity)
- Unclear distinction between source and target domain roles
- Limited justification for when/why the method works better

The evidence demonstrates the technical approach but doesn't fully validate all aspects of the claim's scope.

<score>3</score>

Justification:
1. The evidence shows implementation details matching parts of the claim through:
- Use of clustering to obtain centers (C1...CK)
- Calculation of similarities between input data and centers
- Application of compensation based on these similarities

2. However, several aspects make this moderately overstated:
- The reviewer correctly points out insufficient explanation of fundamental design choices
- The relationship between clustering and transfer demands isn't thoroughly justified
- The evidence doesn't clearly demonstrate why this approach is superior for determining transfer demands compared to alternatives
- The paper itself notes "experiments confirm that DEC performs very well" but doesn't provide comparative analysis

3. The visualization and architecture diagram support the technical implementation but don't fully validate the broader claims about transfer demand determination.


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:

The concept of context based on self-positioning in reference to the anchors (cluster centers) is proposed for figuring out the demanding degree in transferring the representation of each given example.

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: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. Compared with the Pseudo label used in (Zhong & Maki, 2020), we use the similarity between the data example of interest to all the centers from clustering, which figures out the contextual situation regarding feature discriminant more comprehensively than a simple Pseudo label.

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 \(\mathbf{S}=(\mathbf{S}_{1},\mathbf{S}_{2},\ldots,\mathbf{S}_{K})\).

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.

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.

For our solution, the number of cluster centers is set to the true class number of the corresponding data set, which is 256, 200, 120, and 67 for Caltech 256, CUB-200, Stanford Dogs, and MIT Indoor-67, respectively.

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.

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.

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:
Based on my review of the claim, evidence, and reviewer comments, I'll assess whether the claim is well-stated or overstated.

Summary: The claim about using context based on self-positioning relative to cluster centers is generally supported by the evidence presented. The paper provides detailed mathematical formulation and visualization of how the contextual information is derived through clustering and similarity computations. However, several critical weaknesses pointed out by reviewers - particularly the heavy reliance on DEC clustering accuracy and lack of consistent performance across different backbones - suggest some overstatement of the method's effectiveness and generalizability.

Key points supporting this assessment:
1. The evidence demonstrates a clear mathematical framework for using cluster centers as anchors
2. Experimental results and visualizations show the method works in some cases
3. Reviewer concerns about:
   - Heavy dependence on DEC clustering quality
   - Inconsistent performance across different backbones
   - Lack of detailed analysis explaining why the method works
   - More complex architecture than claimed

The claim partially overstates the capability by not acknowledging these important limitations and dependencies.

<score>3</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:

The concept of context based on self-positioning in reference to the anchors (cluster centers) is proposed for figuring out the demanding degree in transferring the representation of each given example.

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: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. Compared with the Pseudo label used in (Zhong & Maki, 2020), we use the similarity between the data example of interest to all the centers from clustering, which figures out the contextual situation regarding feature discriminant more comprehensively than a simple Pseudo label.

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 \(\mathbf{S}=(\mathbf{S}_{1},\mathbf{S}_{2},\ldots,\mathbf{S}_{K})\).

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.

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.

For our solution, the number of cluster centers is set to the true class number of the corresponding data set, which is 256, 200, 120, and 67 for Caltech 256, CUB-200, Stanford Dogs, and MIT Indoor-67, respectively.

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.

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.

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 that it is moderately overstated. While the evidence shows that the authors do propose using cluster centers as anchors for determining context through self-positioning, there are several limitations noted in the review comments that aren't acknowledged in the claim. The evidence demonstrates the technical implementation but doesn't fully validate how effectively this approach determines "demanding degree" in transfer learning. The reviewer points out that the method "barely competes with SOTA methods" and questions whether the compensation networks meaningfully impact results.

Key limitations include:
- Limited empirical validation of the relationship between cluster-based positioning and transfer demands
- Assumptions about classification tasks that aren't addressed in the claim
- Lack of clear demonstration that the context-based approach improves transfer learning outcomes

<score>3</score>

The claim would be better stated if it acknowledged these limitations and more precisely described the proposed mechanism rather than making broader assertions about its effectiveness for transfer learning.


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 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 that the claim accurately reflects the technical approach described in the evidence, without exaggeration. The evidence thoroughly supports the claim through:

1. Detailed mathematical formulation showing how context is incorporated through similarity measures with cluster centers
2. Clear explanation of the soft gating mechanism (W) that controls feature compensation
3. Visual evidence in Figure 2 demonstrating how the soft gating factor varies based on context
4. Architecture diagram in Figure 1 showing the end-to-end learning setup

The claim is well-stated and matches the technical implementation described in the evidence, without overreaching beyond what is demonstrated.

<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:

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 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: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 assess that the claim is moderately overstated based on several factors:

1. While the evidence shows that context and soft gating are indeed incorporated into the model architecture, the claim suggests a more definitive understanding of "the degree that each data example needs transferring" than is supported. The reviewer correctly points out that clustering is performed only on target data, making it impossible to truly assess transfer needs relative to the source distribution.

2. The evidence demonstrates correlation between similarity scores and gating factors, but doesn't conclusively prove the system learns optimal transfer degrees end-to-end, as claimed.

3. The experimental visuals and histogram show the relationship between similarities and gating factors, but as noted in the review comments, theoretical guarantees are missing.

<score>3</score>

The claim would be better stated with more measured language acknowledging these limitations and uncertainties in how effectively the method determines transfer needs.