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:

Based on such a scheme for context description, we propose an end-to-end framework to learn feature compensation for transfer learning with soft gating to decide whether and how much feature compensation is needed, accounting for the mismatch between source domain and target domain.

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

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

Adversarial-based deep transfer learning is based on Generative Adversarial Network (GAN) (Goodfellow et al., 2014) to search the transferring solution from source to target domain. Domain Adversarial Neural Network (DANN) (Ajakan et al., 2014) introduces the idea of GAN to transfer learning by applying a domain classifier to minimize the difference between source and target domain

Network-based deep transfer learning reuses the model pre-trained on source domain, including architecture and parameters, to form a part of the solution of target domain, since filters in convolutional layers act as feature extractors in fact and the feature embedding turned out from them are regarded generic to some extent.

In view of that, \(L^{2}\)-SP (Xuhong et al., 2018) and DELTA (Li et al., 2019) apply regularization to loss function in order to compensate for such difference. GTN (Zhu et al., 2018) holds in terms of feature selection to weight features with gating neural network such that the common features shared by source domain and target domain can receive more attention.

From another point of view, parameter modification on the original network (Xuhong et al., 2018; Li et al., 2019; Zhu et al., 2018; Zhong & Maki, 2020) is not able to tackle all mismatches between source and target domain.

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.

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.

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. Following the aforementioned transfer network, the overall embedding fed to classifier becomes: \[\mathbf{X}+\mathbf{W}.\Delta=\mathbf{X}+MLP(\mathbf{S}).MLP(\mathbf{X}\)()S) \tag{1}\] 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. After applying transfer learning, the classification changes from \(\mathbf{y}=\phi(\mathbf{X})\) to \(\mathbf{y}=\phi(\mathbf{X}+\mathbf{W}.\Delta)\), where \(\phi\) denotes classifier and \(\mathbf{y}\) the class label resulting from the classifier. 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.

* 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.

Here, we compare these methods from two perspectives: (1) We compare different methods with a given backbone network: ResNet-101, ResNet-50, and VGG-16, respectively; (2) We compare the overall performance across all backbone models. **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.

For MIT Indoor-67 data, the target is scene classification, which differs much from the source domain, say, object recognition. This might account for why the other methods fail to outperform fine tune in this case. Note that we annotate \(L^{2}\), \(L^{2}\)-SP and DELTA with * in Table 1 as they use Places 365 (Zhou et al., 2017) as source-domain data for pre-training, which is also a scene classification data. As a result, their performance seems more promising since the source domain and the target domain are focused on the same visual recognition task.

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

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

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

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

Input: Figure 1: Context-directed Transfer Learning

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

Output:
After reviewing the claim and evidence, I find that it is generally well-stated with minimal overstatement. The evidence thoroughly demonstrates:

1. The proposed framework does implement end-to-end feature compensation using soft gating, as shown in Figure 1's architecture.
2. The contextual description scheme is clearly explained through clustering and similarity calculations.
3. Experimental results in Table 1 validate the method's effectiveness.
4. Figure 2 provides empirical evidence supporting how the soft gating adapts compensation based on context.

The claim accurately reflects the technical approach without exaggerating its capabilities or novelty. The description matches the actual implementation detailed in the evidence.

<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:

Based on such a scheme for context description, we propose an end-to-end framework to learn feature compensation for transfer learning with soft gating to decide whether and how much feature compensation is needed, accounting for the mismatch between source domain and target domain.

The review comment to be evaluated is:

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

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

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

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

Adversarial-based deep transfer learning is based on Generative Adversarial Network (GAN) (Goodfellow et al., 2014) to search the transferring solution from source to target domain. Domain Adversarial Neural Network (DANN) (Ajakan et al., 2014) introduces the idea of GAN to transfer learning by applying a domain classifier to minimize the difference between source and target domain

Network-based deep transfer learning reuses the model pre-trained on source domain, including architecture and parameters, to form a part of the solution of target domain, since filters in convolutional layers act as feature extractors in fact and the feature embedding turned out from them are regarded generic to some extent.

In view of that, \(L^{2}\)-SP (Xuhong et al., 2018) and DELTA (Li et al., 2019) apply regularization to loss function in order to compensate for such difference. GTN (Zhu et al., 2018) holds in terms of feature selection to weight features with gating neural network such that the common features shared by source domain and target domain can receive more attention.

From another point of view, parameter modification on the original network (Xuhong et al., 2018; Li et al., 2019; Zhu et al., 2018; Zhong & Maki, 2020) is not able to tackle all mismatches between source and target domain.

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.

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.

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. Following the aforementioned transfer network, the overall embedding fed to classifier becomes: \[\mathbf{X}+\mathbf{W}.\Delta=\mathbf{X}+MLP(\mathbf{S}).MLP(\mathbf{X}\)()S) \tag{1}\] 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. After applying transfer learning, the classification changes from \(\mathbf{y}=\phi(\mathbf{X})\) to \(\mathbf{y}=\phi(\mathbf{X}+\mathbf{W}.\Delta)\), where \(\phi\) denotes classifier and \(\mathbf{y}\) the class label resulting from the classifier. 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.

* 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.

Here, we compare these methods from two perspectives: (1) We compare different methods with a given backbone network: ResNet-101, ResNet-50, and VGG-16, respectively; (2) We compare the overall performance across all backbone models. **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.

For MIT Indoor-67 data, the target is scene classification, which differs much from the source domain, say, object recognition. This might account for why the other methods fail to outperform fine tune in this case. Note that we annotate \(L^{2}\), \(L^{2}\)-SP and DELTA with * in Table 1 as they use Places 365 (Zhou et al., 2017) as source-domain data for pre-training, which is also a scene classification data. As a result, their performance seems more promising since the source domain and the target domain are focused on the same visual recognition task.

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

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

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

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

Input: Figure 1: Context-directed Transfer Learning

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

Output:
After reviewing the claim and evidence, this appears to be a moderately overstated claim. While the evidence shows the authors did develop an end-to-end framework using soft gating for feature compensation, there are several concerns raised in the review that weaken the claim's validity:

1. The clustering is performed only on target data, making it questionable whether it truly captures source-target domain mismatch as claimed
2. Poor performance on MIT Indoor-67 suggests limitations when domains are very different
3. Lack of theoretical analysis or guarantees for the methodology
4. Unclear which of the proposed training strategies works best

The experimental results show improvements in some cases but aren't consistently superior across all datasets and backbones, making the broad claim about adaptive feature compensation somewhat overstated.

<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:

Based on such a scheme for context description, we propose an end-to-end framework to learn feature compensation for transfer learning with soft gating to decide whether and how much feature compensation is needed, accounting for the mismatch between source domain and target domain.

The review comment to be evaluated is:

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

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

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

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

Adversarial-based deep transfer learning is based on Generative Adversarial Network (GAN) (Goodfellow et al., 2014) to search the transferring solution from source to target domain. Domain Adversarial Neural Network (DANN) (Ajakan et al., 2014) introduces the idea of GAN to transfer learning by applying a domain classifier to minimize the difference between source and target domain

Network-based deep transfer learning reuses the model pre-trained on source domain, including architecture and parameters, to form a part of the solution of target domain, since filters in convolutional layers act as feature extractors in fact and the feature embedding turned out from them are regarded generic to some extent.

In view of that, \(L^{2}\)-SP (Xuhong et al., 2018) and DELTA (Li et al., 2019) apply regularization to loss function in order to compensate for such difference. GTN (Zhu et al., 2018) holds in terms of feature selection to weight features with gating neural network such that the common features shared by source domain and target domain can receive more attention.

From another point of view, parameter modification on the original network (Xuhong et al., 2018; Li et al., 2019; Zhu et al., 2018; Zhong & Maki, 2020) is not able to tackle all mismatches between source and target domain.

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.

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.

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. Following the aforementioned transfer network, the overall embedding fed to classifier becomes: \[\mathbf{X}+\mathbf{W}.\Delta=\mathbf{X}+MLP(\mathbf{S}).MLP(\mathbf{X}\)()S) \tag{1}\] 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. After applying transfer learning, the classification changes from \(\mathbf{y}=\phi(\mathbf{X})\) to \(\mathbf{y}=\phi(\mathbf{X}+\mathbf{W}.\Delta)\), where \(\phi\) denotes classifier and \(\mathbf{y}\) the class label resulting from the classifier. 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.

* 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.

Here, we compare these methods from two perspectives: (1) We compare different methods with a given backbone network: ResNet-101, ResNet-50, and VGG-16, respectively; (2) We compare the overall performance across all backbone models. **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.

For MIT Indoor-67 data, the target is scene classification, which differs much from the source domain, say, object recognition. This might account for why the other methods fail to outperform fine tune in this case. Note that we annotate \(L^{2}\), \(L^{2}\)-SP and DELTA with * in Table 1 as they use Places 365 (Zhou et al., 2017) as source-domain data for pre-training, which is also a scene classification data. As a result, their performance seems more promising since the source domain and the target domain are focused on the same visual recognition task.

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

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

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

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

Input: Figure 1: Context-directed Transfer Learning

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

Output:
Based on my review of the claim, evidence, and reviewer comments, here is my assessment:

The claim appears somewhat overstated given the available evidence and reviewer concerns. While the paper presents a framework for feature compensation with soft gating, several key weaknesses are noted: 1) Limited technical explanations and justifications for design choices, 2) Unclear distinction between source/target domain contributions, and 3) Lack of analysis regarding when/why the method works vs fails. The experimental results show mixed performance across datasets, with improvements in some cases but not consistently outperforming baselines. The vague explanations about the method's inner workings make it difficult to fully validate the claimed capabilities.

<score>4</score>

Justification:
- The claim broadly states the framework can "learn feature compensation" and "decide whether and how much feature compensation is needed" but provides insufficient technical rationale for how these decisions are made
- Reviewer points out critical gaps in explaining why certain components (DEC, cosine similarity) were chosen and how mismatch is actually identified
- Results are mixed across datasets with some improvements but also cases where the method underperforms simpler baselines
- The paper lacks clear analysis of the conditions under which the proposed approach is effective vs. not effective
- The evidence shows working implementations but doesn't fully support the broad capability claims made


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:

Based on such a scheme for context description, we propose an end-to-end framework to learn feature compensation for transfer learning with soft gating to decide whether and how much feature compensation is needed, accounting for the mismatch between source domain and target domain.

The review comment to be evaluated is:

### Strength

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

### Weakness

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

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

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

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

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

Adversarial-based deep transfer learning is based on Generative Adversarial Network (GAN) (Goodfellow et al., 2014) to search the transferring solution from source to target domain. Domain Adversarial Neural Network (DANN) (Ajakan et al., 2014) introduces the idea of GAN to transfer learning by applying a domain classifier to minimize the difference between source and target domain

Network-based deep transfer learning reuses the model pre-trained on source domain, including architecture and parameters, to form a part of the solution of target domain, since filters in convolutional layers act as feature extractors in fact and the feature embedding turned out from them are regarded generic to some extent.

In view of that, \(L^{2}\)-SP (Xuhong et al., 2018) and DELTA (Li et al., 2019) apply regularization to loss function in order to compensate for such difference. GTN (Zhu et al., 2018) holds in terms of feature selection to weight features with gating neural network such that the common features shared by source domain and target domain can receive more attention.

From another point of view, parameter modification on the original network (Xuhong et al., 2018; Li et al., 2019; Zhu et al., 2018; Zhong & Maki, 2020) is not able to tackle all mismatches between source and target domain.

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.

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.

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. Following the aforementioned transfer network, the overall embedding fed to classifier becomes: \[\mathbf{X}+\mathbf{W}.\Delta=\mathbf{X}+MLP(\mathbf{S}).MLP(\mathbf{X}\)()S) \tag{1}\] 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. After applying transfer learning, the classification changes from \(\mathbf{y}=\phi(\mathbf{X})\) to \(\mathbf{y}=\phi(\mathbf{X}+\mathbf{W}.\Delta)\), where \(\phi\) denotes classifier and \(\mathbf{y}\) the class label resulting from the classifier. 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.

* 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.

Here, we compare these methods from two perspectives: (1) We compare different methods with a given backbone network: ResNet-101, ResNet-50, and VGG-16, respectively; (2) We compare the overall performance across all backbone models. **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.

For MIT Indoor-67 data, the target is scene classification, which differs much from the source domain, say, object recognition. This might account for why the other methods fail to outperform fine tune in this case. Note that we annotate \(L^{2}\), \(L^{2}\)-SP and DELTA with * in Table 1 as they use Places 365 (Zhou et al., 2017) as source-domain data for pre-training, which is also a scene classification data. As a result, their performance seems more promising since the source domain and the target domain are focused on the same visual recognition task.

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

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

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

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

Input: Figure 1: Context-directed Transfer Learning

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

Output:
Based on my review of the claim, evidence, and reviewer comments, I find the claim to be moderately overstated.

The claim suggests an "end-to-end framework" with "soft gating" that effectively handles domain mismatch. However, several issues emerge:

1. The reviewer correctly points out that the method's success heavily depends on DEC clustering results, making it less "end-to-end" than claimed.

2. The experimental results show inconsistent performance across different backbones - working well with ResNet-101 but underperforming with ResNet-50 and VGG16.

3. The framework is more complex than presented, requiring multiple fine-tuning steps and additional components beyond just "soft gating."

4. The claim doesn't acknowledge these limitations or the method's heavy computational requirements.

<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:

Based on such a scheme for context description, we propose an end-to-end framework to learn feature compensation for transfer learning with soft gating to decide whether and how much feature compensation is needed, accounting for the mismatch between source domain and target domain.

The review comment to be evaluated is:

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

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

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

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

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

Adversarial-based deep transfer learning is based on Generative Adversarial Network (GAN) (Goodfellow et al., 2014) to search the transferring solution from source to target domain. Domain Adversarial Neural Network (DANN) (Ajakan et al., 2014) introduces the idea of GAN to transfer learning by applying a domain classifier to minimize the difference between source and target domain

Network-based deep transfer learning reuses the model pre-trained on source domain, including architecture and parameters, to form a part of the solution of target domain, since filters in convolutional layers act as feature extractors in fact and the feature embedding turned out from them are regarded generic to some extent.

In view of that, \(L^{2}\)-SP (Xuhong et al., 2018) and DELTA (Li et al., 2019) apply regularization to loss function in order to compensate for such difference. GTN (Zhu et al., 2018) holds in terms of feature selection to weight features with gating neural network such that the common features shared by source domain and target domain can receive more attention.

From another point of view, parameter modification on the original network (Xuhong et al., 2018; Li et al., 2019; Zhu et al., 2018; Zhong & Maki, 2020) is not able to tackle all mismatches between source and target domain.

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.

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.

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. Following the aforementioned transfer network, the overall embedding fed to classifier becomes: \[\mathbf{X}+\mathbf{W}.\Delta=\mathbf{X}+MLP(\mathbf{S}).MLP(\mathbf{X}\)()S) \tag{1}\] 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. After applying transfer learning, the classification changes from \(\mathbf{y}=\phi(\mathbf{X})\) to \(\mathbf{y}=\phi(\mathbf{X}+\mathbf{W}.\Delta)\), where \(\phi\) denotes classifier and \(\mathbf{y}\) the class label resulting from the classifier. 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.

* 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.

Here, we compare these methods from two perspectives: (1) We compare different methods with a given backbone network: ResNet-101, ResNet-50, and VGG-16, respectively; (2) We compare the overall performance across all backbone models. **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.

For MIT Indoor-67 data, the target is scene classification, which differs much from the source domain, say, object recognition. This might account for why the other methods fail to outperform fine tune in this case. Note that we annotate \(L^{2}\), \(L^{2}\)-SP and DELTA with * in Table 1 as they use Places 365 (Zhou et al., 2017) as source-domain data for pre-training, which is also a scene classification data. As a result, their performance seems more promising since the source domain and the target domain are focused on the same visual recognition task.

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

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

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

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

Input: Figure 1: Context-directed Transfer Learning

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

Output:
After reviewing the evidence, the claim appears partially overstated. While the paper does present an end-to-end framework using soft gating for feature compensation, several key concerns emerge:

1. The reviewer points out that the relationship between data centers and transfer learning is poorly explained, with unclear methodology around target dataset computation.

2. The experimental results show the method "barely competes with SOTA" and the compensation networks don't significantly improve results.

3. While the paper provides mathematical formulations and visualizations, the evidence doesn't fully demonstrate the effectiveness of the feature compensation approach compared to simpler fine-tuning.

The framework exists as claimed, but its benefits and novelty appear overstated given the empirical results and reviewer feedback.

<score>4</score>

Justification:
1. The claim overstates the effectiveness by not acknowledging limitations highlighted in the review, particularly around methodology clarity and performance gains.

2. The evidence shows mixed results - while there are some improvements on certain datasets (e.g., CUB-200 with ResNet-101), many results are comparable to or worse than baselines.

3. The claim's strong emphasis on feature compensation effectiveness isn't fully supported by the experimental evidence, which shows modest improvements at best.

4. The paper doesn't adequately address the reviewer's concerns about the fundamental assumptions and generalizability of the approach.

5. Direct citations from Table 1 show inconsistent performance improvements, with many cases where the method performs similarly to fine-tuning, contradicting the implied significance in the claim.


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 approach that, we add only two subnetworks in the form of Multilayer Perceptron (MLP) to backbone network, one for computing the feature compensation and the other for soft gating the compensation, where both are computed based on the context, that is, the feature compensation is context adaptive.

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.

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.

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.

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.

Figure 2(b) shows the 2-dimensional histogram of maximum cosine similarity against soft gating factor \(\mathbf{W}\), which shows such a trend that lower similarity corresponds with in general higher compensation level, say, higher soft gating. Since the maximum similarity to cluster centers indicates the membership degree of the data example in association with a deterministic cluster, and a smaller value means a higher ambiguous case in terms of membership association, it is thus natural to result in higher-level transferring for feature compensation. This fully exhibits the motivation of this study, that is, why we make use of clustering centers as the anchors to figure out the contextual clue in directing feature compensation.

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

The strong point aspect as well as the weakness lies in the clustering-based contextual description, which determines the overall performance.

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 generally well-stated with minimal overstatement. The evidence clearly shows that:

1. Two MLP subnetworks are indeed added to the backbone network (shown in Figure 1)
2. One MLP computes feature compensation (Δ)
3. One MLP computes soft gating (W)
4. Both computations are context-based, using similarity scores S derived from cluster centers

The claim accurately represents the architectural additions and their purposes without exaggerating their complexity or impact. The evidence in both the text and diagrams directly supports each component mentioned in the claim.

<score>1</score>

The claim is scored as 1 (well-stated) because it:
- Makes specific, verifiable statements about the network architecture
- Accurately describes the role of each component
- Avoids overstating capabilities or results
- Is fully supported by the detailed evidence provided in the text and figures


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 approach that, we add only two subnetworks in the form of Multilayer Perceptron (MLP) to backbone network, one for computing the feature compensation and the other for soft gating the compensation, where both are computed based on the context, that is, the feature compensation is context adaptive.

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.

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.

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.

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.

Figure 2(b) shows the 2-dimensional histogram of maximum cosine similarity against soft gating factor \(\mathbf{W}\), which shows such a trend that lower similarity corresponds with in general higher compensation level, say, higher soft gating. Since the maximum similarity to cluster centers indicates the membership degree of the data example in association with a deterministic cluster, and a smaller value means a higher ambiguous case in terms of membership association, it is thus natural to result in higher-level transferring for feature compensation. This fully exhibits the motivation of this study, that is, why we make use of clustering centers as the anchors to figure out the contextual clue in directing feature compensation.

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

The strong point aspect as well as the weakness lies in the clustering-based contextual description, which determines the overall performance.

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 they do add two MLP subnetworks (one for compensation and one for soft gating) to the backbone network, the claim that "both are computed based on the context" oversimplifies the actual mechanism. The evidence shows that only the soft gating (W) is computed purely from context (S), while the feature compensation (Δ) is computed from both the original features (X) and context (S) via concatenation. Additionally, per reviewer comment 1, the method's effectiveness is questioned when source and target distributions differ significantly.

<score>3</score>

Justification:
1. The claim correctly states the addition of two MLP subnetworks
2. However, it oversimplifies by stating both are "computed based on the context" when:
   - Evidence shows Δ = MLP(X⊕S) uses both features and context
   - Only W = MLP(S) is purely context-based
3. The reviewer's point about target-only clustering undermining the "how much transferring is needed" aspect suggests some overstatement in the adaptive nature claim
4. The evidence from Figure 2(b) supports context-adaptive behavior, but with limitations noted in the reviews regarding theoretical guarantees and performance across different domains