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:

To enable identifying the position of the input in reference to the overall data distribution of source domain, we perform clustering at first to figure out the data distribution in a compact form represented by cluster centers, and then use the similarities between the input and the cluster centers to describe the relative position of the input in reference to the cluster centers.

The evidence to be evaluated is: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.

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.

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 1: Context-directed Transfer Learning

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

Output:
I'll evaluate the claim about using clustering to identify input positions relative to data distribution:

The evidence strongly supports the claim through:
1. Detailed mathematical description of how clustering centers (C1...CK) are used as reference points
2. Clear explanation of cosine similarity calculations between input X and cluster centers
3. Figure 1 showing the technical implementation with clustering (DEC) and similarity computations
4. Specific details about cluster numbers used for different datasets
5. Explanation of how similarity values indicate membership degree and guide feature compensation

The claim accurately represents the methodology without exaggeration, clearly stating the two-step process (clustering followed by similarity calculation) and its purpose. The evidence provides comprehensive technical details that directly validate the claim's components.

<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:

To enable identifying the position of the input in reference to the overall data distribution of source domain, we perform clustering at first to figure out the data distribution in a compact form represented by cluster centers, and then use the similarities between the input and the cluster centers to describe the relative position of the input in reference to the cluster centers.

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: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.

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.

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 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 the claim accurately reflects the evidence:

Summary: The claim is moderately overstated based on several key issues:
1. While the evidence shows they do perform clustering and similarity calculations, the reviewer correctly points out that clustering is performed on target data, not source domain data, contradicting the claim about "identifying position in reference to source domain."
2. The evidence demonstrates the technical implementation but doesn't fully support the claimed purpose of reference to "overall data distribution of source domain."
3. The methodology is technically described accurately, but its actual effectiveness for the stated purpose is questioned by empirical results (poor performance on MIT Indoor-67) mentioned in the review.

<score>4</score>

The score of 4 reflects that while the technical description is accurate, the claim overstates the method's ability to identify positions relative to the source domain distribution, when in fact it works with target domain clustering.


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:

To enable identifying the position of the input in reference to the overall data distribution of source domain, we perform clustering at first to figure out the data distribution in a compact form represented by cluster centers, and then use the similarities between the input and the cluster centers to describe the relative position of the input in reference to the cluster centers.

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: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.

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.

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 1: Context-directed Transfer Learning

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

Output:
After reviewing the claim, evidence, and reviewer comments, I'll evaluate whether the claim is well-stated or overstated.

Summary: The claim describes using clustering to identify input positions relative to data distribution through cluster centers and similarities. The evidence provides substantial technical details about the implementation, including the use of DEC clustering, cosine similarity measurements, and specific cluster center numbers for different datasets. However, the reviewer comments highlight significant gaps in explaining why certain technical decisions were made and how the method specifically helps identify source-domain/target-domain mismatches.

Justification:
1. The claim is supported by detailed technical implementation in the evidence, showing how clustering and similarity measurements are used.
2. The paper provides concrete examples of cluster center numbers for different datasets, demonstrating practical application.
3. However, the reviewers point out valid concerns about:
   - Lack of explanation for why DEC clustering was chosen
   - Unclear distinction between source and target domain roles
   - Missing justification for using cosine similarity
4. The evidence shows the mechanistic aspects but doesn't fully justify the conceptual decisions.

<score>3</score>

The claim is moderately overstated. While the technical implementation is well-documented, the claim would be stronger if it acknowledged the limitations and provided clearer justification for the methodological choices, as highlighted in the reviewer comments.


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:

To enable identifying the position of the input in reference to the overall data distribution of source domain, we perform clustering at first to figure out the data distribution in a compact form represented by cluster centers, and then use the similarities between the input and the cluster centers to describe the relative position of the input in reference to the cluster centers.

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: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.

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.

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 1: Context-directed Transfer Learning

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

Output:
After reviewing the claim and evidence, I find the claim is moderately overstated. While the evidence shows that clustering and similarity calculations are indeed used to identify input positions relative to cluster centers, several key concerns emerge:

1. The review points out that "it remains unclear whether clustering can model the context information" effectively.
2. The evidence heavily relies on DEC for clustering success, but potential limitations and failure cases aren't acknowledged in the claim.
3. The claim presents the approach as definitive, but experimental evidence shows inconsistent performance across different backbones.
4. The methodology is more complex than the claim suggests, involving additional components like autoencoder.

The claim would be better stated with appropriate caveats about limitations and dependencies.

<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:

To enable identifying the position of the input in reference to the overall data distribution of source domain, we perform clustering at first to figure out the data distribution in a compact form represented by cluster centers, and then use the similarities between the input and the cluster centers to describe the relative position of the input in reference to the cluster centers.

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: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.

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.

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 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 while the basic concept is supported, there are significant concerns raised in the review comments about the effectiveness and broad applicability of the approach. The evidence shows a detailed technical description of how clustering and similarity measurements are used to determine input positions relative to cluster centers. However, the review points out crucial gaps regarding how this relates to data transfer and questions the method's effectiveness given experimental results.

The evidence provides mathematical formulation and technical details of the clustering approach, but doesn't fully address:
1. The relationship between clustering and effective data transfer
2. Justification for why distance-based compensation works
3. Limitations of the approach and its applicability beyond image classification

The claim somewhat overstates the capabilities by presenting the clustering approach as a comprehensive solution without acknowledging these limitations and uncertainties.

<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:

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 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 claim and evidence, I notice several key points:

1. While the network changes are relatively minor (adding context-aware components), calling them "tiny" understates their complexity - they involve clustering, auto-encoders, and MLPs.

2. The "big performance improvement" claim is only partially supported. Tables show mixed results - some improvements are modest (1-2%) while others are more substantial (4%). Not all comparisons show superiority.

3. "Minimum effort" is debatable given the solution requires careful design of context components, clustering, and multiple training strategies (I, II, III, I+).

The claim simplifies and overstates both the simplicity of changes and consistency of improvements.

<score>4</score>

Justification:
1. The evidence shows more complex architectural changes than claimed:
- Requires DEC auto-encoder and clustering components
- Uses multiple MLPs and similarity computations
- Has 4 different training strategies (I, II, III, I+)

2. Performance claims are overstated:
- Table 1 shows varied results across datasets and backbones
- Some improvements are modest (e.g., 83.98% vs 83.83% on CUB-200 with ResNet50)
- Not consistently outperforming all baselines (e.g., TransTailor better on some VGG16 results)

3. "Minimum effort" claim contradicts evidence:
- Complex context-aware system with multiple components
- Requires careful tuning of clustering and feature dimensionality
- Multiple training strategies needed for optimal results

4. Notable lack of:
- Statistical significance testing
- Computational cost analysis
- Clear comparison of implementation complexity

The evidence supports that the method works but doesn't fully justify claims about minimal changes and consistent large improvements.


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

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

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

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 claim and evidence, I find several aspects that suggest overstatement:

1. The claim of "minimum effort" is contradicted by the complex architecture shown in Figure 1, which includes multiple components (clustering, auto-encoder, MLPs, etc.).

2. While performance improvements are shown in some cases (e.g., CUB-200 and Stanford Dogs datasets), they are not consistently "big" across all datasets and backbones as claimed. For MIT Indoor-67, the method performs poorly, which is acknowledged in the review comments.

3. The review points out valid concerns about the theoretical foundations and appropriateness of the method when source and target distributions differ significantly.

4. The characterization of "two tiny components" understates the actual complexity of the proposed architecture.

<score>4</score>

The claim overstates both the simplicity of the approach and the consistency/magnitude of its performance improvements. While the method shows promise in certain scenarios, the evidence suggests more measured conclusions would be appropriate.


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

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

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

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 claim and evidence, I find the claim to be moderately overstated. While the evidence shows performance improvements on several datasets, the claim of "big performance improvement" is not consistently supported across all experiments. The improvements are modest in many cases (1-4%). Additionally, the review comments highlight significant gaps in explaining technical decisions and lack of clear reasoning for when/why the method works better. The claim of "minimum effort" is also questionable given the added complexity of clustering, similarity computation, and multiple MLP components.

<score>4</score>

Specific issues:
1. The term "big performance improvement" exaggerates the actual results, which show variable and sometimes minor gains
2. "Minimum effort" downplays the added complexity of the proposed architecture
3. Claiming "sound solution" without adequately explaining technical choices (as noted in reviews)
4. Limited acknowledgment of limitations and cases where the method underperforms
5. The evidence shows mixed results across different datasets and backbones, not consistently supporting the broad claims made

Citations:
- Table 1 shows improvements ranging from ~1-4% in most cases
- Reviewer notes "very unclear [...] how the proposed method is actually helping"
- Reviewer highlights lack of "reasoning as to what the proposed method works in some cases but not in others"