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

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

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

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

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

The claim to be assessed is:

The new architecture only incorporates two additional MLP modules, one for computing feature compensation and the other for soft gating the compensation.

The review comment to be evaluated is:

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

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

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

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

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.

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.

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

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

Input: Figure 1: Context-directed Transfer Learning

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

Output:
After reviewing the claim and evidence, I'll assess whether it accurately reflects the architectural additions described:

The evidence, particularly Figure 1, shows that while there are indeed two MLP modules (one for compensation Δ and one for soft gating W), the complete architecture includes several other essential components:
- An Auto-Encoder (DEC)
- A clustering mechanism
- Cosine similarity calculation
- Feature concatenation operations

While the claim correctly identifies the two MLP modules, it significantly understates the complexity by omitting these other crucial architectural components that are necessary for the system to function. The review comment also highlights this oversimplification by noting questions about how the data centers and feature vectors are computed.

<score>4</score>

This claim receives a 4 (significantly overstated) because it oversimplifies the architectural additions by focusing only on the MLPs while omitting several other essential components revealed in the evidence.


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

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

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

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

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

The claim to be assessed is:

This meets almost all the requirements 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. The existing methods can be sorted into 4 categories (Tan et al., 2018): Instances-based, mapping-based, adversarial-based, and network-based deep transfer learning. Instances-based deep transfer learning aims to reuse the instances of source domain. For example, TrAdaBoost (Dai Wenyuan et al., 2007) weights the instances of source domain to make the distribution similar to that of target domain, and perform model training based on the weighted instances from source domain along with those from target domain. 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. A representative work in this direction is Transfer Component Analysis (Pan et al., 2010), and Tzeng et al. (Tzeng et al., 2014) extents it to deep learning based solutions. 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 in feature learning, that is, optimization on transferred feature representation is driven by not only classification but also removing the gap between source and target domain. 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. An early work for speech recognition (Huang et al., 2013) partitions the neural networks of a solution into two parts: Language-irrelevant and language relevant, and reuse the language-irrelevant part for other recognition tasks. Oquab et al. (Oquab et al., 2014) reuses the Convolutional Neural Network (CNN) pre-trained on ImageNet with additional fully connected layers to adapt to target-domain classification. Another classical method, namely fine tune (Yosinski et al., 2014), reuses the original network without any change on the structure and just modifies the parameter values with the data from target domain, where fine tune can be performed on the whole network, or a part of them after fixing the other part.

Head2Toe (Evci et al., 2022) makes use of the representations from all layers and applies Lasso to obtain feature selection in their transfer learning framework.

Hereafter, AdaFilter (Guo et al., 2020) and TransTailor (Liu et al., 2021) alleviate this by reorganizing the network architecture.

AdaFilter applies Layer-wise Recurrent Gated Network to select reusable filters in original solution to form a new pipeline as target-domain solution. TransTailor introduces pruning to make network structure as concise as enough for target domain, which leads to a optimal sub-model.

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. If its position is closer to an existing cluster center, in general, a higher probability of correct classification can be expected due to the explicit association with known data representatives. In such a case, the embedding should be changed little. On the contrary, the embedding should be remedied remarkably if its association to any cluster center is not so deterministic. 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.

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

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

Here, we make use of the algorithm referred to as Deep Embedded Clustering (DEC) (Xie et al., 2016) to obtain such anchor points, and the experiments confirm that DEC performs very well in the proposed framework. The proposed transfer learning framework can work in 4 manners to form the following solutions:* (I) At first, fine tune the backbone network, and then fix the backbone and train the transfer network only.

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

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

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

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

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

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

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

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

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

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

**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 Caltech 256-30 data, only 30 data samples are randomly selected per class for training. This degrades our method much because we use clustering to select representatives but the random selection prevents our method from finding the truly representative examples due to the too sparse random sampling of 30 data examples per class in contrast to the 30,607 images in total. To verify this issue, we reestablish our solution (I) by training the clustering model with all the data samples other than the testing samples, which is referred to as (I+) in Table 1.

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

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

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

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

We propose a new transfer learning method with context-aware compensation to feature embedding, where a contextual descriptor figuring out how close from the input to each cluster center is applied, and soft gating is used to make the feature transfer focused on the data samples that truly need transferring. The experiments demonstrate that the proposed feature transferring scheme is technically sound, and a big performance improvement is achieved using a tiny network.

* Liu et al. (2021) Bingyan Liu, Yifeng Cai, Yao Guo, and Xiangqun Chen. Transtailor: Pruning the pre-trained model for improved transfer learning. In _Proceedings of the AAAI Conference on Artificial Intelligence_, volume 35, pp. 8627-8634, 2021.

* Oquab et al. (2014) Maxime Oquab, Leon Bottou, Ivan Laptev, and Josef Sivic. Learning and transferring mid-level image representations using convolutional neural networks. In _Proceedings of the IEEE conference on computer vision and pattern recognition_, pp. 1717-1724, 2014. \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 this is a moderately overstated claim. While the paper does present a novel transfer learning approach with strong empirical results, there are several limitations and nuances:

1. Performance varies significantly across datasets and architectures - not consistently superior
2. For some datasets (like Caltech 256-30), the method requires additional data (+) to achieve competitive results
3. The improvements are modest in many cases, and some baseline methods outperform it on certain tasks
4. The evidence focuses mainly on image classification tasks, limiting generalizability claims
5. The method requires additional computation for clustering and context calculation

The evidence suggests this is a valuable contribution but not a comprehensive solution meeting "almost all" transfer learning requirements. A more measured claim about specific strengths would be more appropriate.

<score>4</score>


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

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

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

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

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

The claim to be assessed is:

This meets almost all the requirements of transfer learning:

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. The existing methods can be sorted into 4 categories (Tan et al., 2018): Instances-based, mapping-based, adversarial-based, and network-based deep transfer learning. Instances-based deep transfer learning aims to reuse the instances of source domain. For example, TrAdaBoost (Dai Wenyuan et al., 2007) weights the instances of source domain to make the distribution similar to that of target domain, and perform model training based on the weighted instances from source domain along with those from target domain. 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. A representative work in this direction is Transfer Component Analysis (Pan et al., 2010), and Tzeng et al. (Tzeng et al., 2014) extents it to deep learning based solutions. 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 in feature learning, that is, optimization on transferred feature representation is driven by not only classification but also removing the gap between source and target domain. 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. An early work for speech recognition (Huang et al., 2013) partitions the neural networks of a solution into two parts: Language-irrelevant and language relevant, and reuse the language-irrelevant part for other recognition tasks. Oquab et al. (Oquab et al., 2014) reuses the Convolutional Neural Network (CNN) pre-trained on ImageNet with additional fully connected layers to adapt to target-domain classification. Another classical method, namely fine tune (Yosinski et al., 2014), reuses the original network without any change on the structure and just modifies the parameter values with the data from target domain, where fine tune can be performed on the whole network, or a part of them after fixing the other part.

Head2Toe (Evci et al., 2022) makes use of the representations from all layers and applies Lasso to obtain feature selection in their transfer learning framework.

Hereafter, AdaFilter (Guo et al., 2020) and TransTailor (Liu et al., 2021) alleviate this by reorganizing the network architecture.

AdaFilter applies Layer-wise Recurrent Gated Network to select reusable filters in original solution to form a new pipeline as target-domain solution. TransTailor introduces pruning to make network structure as concise as enough for target domain, which leads to a optimal sub-model.

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. If its position is closer to an existing cluster center, in general, a higher probability of correct classification can be expected due to the explicit association with known data representatives. In such a case, the embedding should be changed little. On the contrary, the embedding should be remedied remarkably if its association to any cluster center is not so deterministic. 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.

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

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

Here, we make use of the algorithm referred to as Deep Embedded Clustering (DEC) (Xie et al., 2016) to obtain such anchor points, and the experiments confirm that DEC performs very well in the proposed framework. The proposed transfer learning framework can work in 4 manners to form the following solutions:* (I) At first, fine tune the backbone network, and then fix the backbone and train the transfer network only.

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

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

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

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

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

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

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

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

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

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

**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 Caltech 256-30 data, only 30 data samples are randomly selected per class for training. This degrades our method much because we use clustering to select representatives but the random selection prevents our method from finding the truly representative examples due to the too sparse random sampling of 30 data examples per class in contrast to the 30,607 images in total. To verify this issue, we reestablish our solution (I) by training the clustering model with all the data samples other than the testing samples, which is referred to as (I+) in Table 1.

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

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

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

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

We propose a new transfer learning method with context-aware compensation to feature embedding, where a contextual descriptor figuring out how close from the input to each cluster center is applied, and soft gating is used to make the feature transfer focused on the data samples that truly need transferring. The experiments demonstrate that the proposed feature transferring scheme is technically sound, and a big performance improvement is achieved using a tiny network.

* Liu et al. (2021) Bingyan Liu, Yifeng Cai, Yao Guo, and Xiangqun Chen. Transtailor: Pruning the pre-trained model for improved transfer learning. In _Proceedings of the AAAI Conference on Artificial Intelligence_, volume 35, pp. 8627-8634, 2021.

* Oquab et al. (2014) Maxime Oquab, Leon Bottou, Ivan Laptev, and Josef Sivic. Learning and transferring mid-level image representations using convolutional neural networks. In _Proceedings of the IEEE conference on computer vision and pattern recognition_, pp. 1717-1724, 2014. \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 "This meets almost all the requirements of transfer learning" appears to be overstated. While the method shows promising results in some scenarios (like CUB-200 and Stanford Dogs datasets), it has notable limitations:

1. The reviewer points out that the method may be inappropriate when source and target distributions differ significantly (evidenced by poor MIT Indoor-67 results)
2. Lacks theoretical guarantees and is presented as a heuristic
3. The experimental results aren't fully convincing regarding which training strategy works best
4. The clustering approach using only target data may not effectively assess transfer needs

The method shows some merit but claiming it meets "almost all requirements" overstates its capabilities and ignores its limitations.

<score>4</score>


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

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

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

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

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

The claim to be assessed is:

This meets almost all the requirements of transfer learning:

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. The existing methods can be sorted into 4 categories (Tan et al., 2018): Instances-based, mapping-based, adversarial-based, and network-based deep transfer learning. Instances-based deep transfer learning aims to reuse the instances of source domain. For example, TrAdaBoost (Dai Wenyuan et al., 2007) weights the instances of source domain to make the distribution similar to that of target domain, and perform model training based on the weighted instances from source domain along with those from target domain. 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. A representative work in this direction is Transfer Component Analysis (Pan et al., 2010), and Tzeng et al. (Tzeng et al., 2014) extents it to deep learning based solutions. 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 in feature learning, that is, optimization on transferred feature representation is driven by not only classification but also removing the gap between source and target domain. 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. An early work for speech recognition (Huang et al., 2013) partitions the neural networks of a solution into two parts: Language-irrelevant and language relevant, and reuse the language-irrelevant part for other recognition tasks. Oquab et al. (Oquab et al., 2014) reuses the Convolutional Neural Network (CNN) pre-trained on ImageNet with additional fully connected layers to adapt to target-domain classification. Another classical method, namely fine tune (Yosinski et al., 2014), reuses the original network without any change on the structure and just modifies the parameter values with the data from target domain, where fine tune can be performed on the whole network, or a part of them after fixing the other part.

Head2Toe (Evci et al., 2022) makes use of the representations from all layers and applies Lasso to obtain feature selection in their transfer learning framework.

Hereafter, AdaFilter (Guo et al., 2020) and TransTailor (Liu et al., 2021) alleviate this by reorganizing the network architecture.

AdaFilter applies Layer-wise Recurrent Gated Network to select reusable filters in original solution to form a new pipeline as target-domain solution. TransTailor introduces pruning to make network structure as concise as enough for target domain, which leads to a optimal sub-model.

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. If its position is closer to an existing cluster center, in general, a higher probability of correct classification can be expected due to the explicit association with known data representatives. In such a case, the embedding should be changed little. On the contrary, the embedding should be remedied remarkably if its association to any cluster center is not so deterministic. 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.

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

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

Here, we make use of the algorithm referred to as Deep Embedded Clustering (DEC) (Xie et al., 2016) to obtain such anchor points, and the experiments confirm that DEC performs very well in the proposed framework. The proposed transfer learning framework can work in 4 manners to form the following solutions:* (I) At first, fine tune the backbone network, and then fix the backbone and train the transfer network only.

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

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

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

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

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

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

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

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

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

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

**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 Caltech 256-30 data, only 30 data samples are randomly selected per class for training. This degrades our method much because we use clustering to select representatives but the random selection prevents our method from finding the truly representative examples due to the too sparse random sampling of 30 data examples per class in contrast to the 30,607 images in total. To verify this issue, we reestablish our solution (I) by training the clustering model with all the data samples other than the testing samples, which is referred to as (I+) in Table 1.

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

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

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

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

We propose a new transfer learning method with context-aware compensation to feature embedding, where a contextual descriptor figuring out how close from the input to each cluster center is applied, and soft gating is used to make the feature transfer focused on the data samples that truly need transferring. The experiments demonstrate that the proposed feature transferring scheme is technically sound, and a big performance improvement is achieved using a tiny network.

* Liu et al. (2021) Bingyan Liu, Yifeng Cai, Yao Guo, and Xiangqun Chen. Transtailor: Pruning the pre-trained model for improved transfer learning. In _Proceedings of the AAAI Conference on Artificial Intelligence_, volume 35, pp. 8627-8634, 2021.

* Oquab et al. (2014) Maxime Oquab, Leon Bottou, Ivan Laptev, and Josef Sivic. Learning and transferring mid-level image representations using convolutional neural networks. In _Proceedings of the IEEE conference on computer vision and pattern recognition_, pp. 1717-1724, 2014. \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 and reviewer comments, the claim that "This meets almost all the requirements of transfer learning" appears overstated. While the paper presents a novel approach using context-aware compensation and clustering, several critical issues are identified in the reviewer comments: lack of clear explanations for technical decisions, unclear distinction between source and target domain operations, and insufficient analysis of when/why the method works or fails. The experimental results show mixed performance - sometimes outperforming baselines but underperforming in other scenarios (e.g., MIT Indoor-67, limited data cases).

<score>4</score>

Justification:
1. The reviewer raises valid concerns about insufficient explanation of key technical decisions (why DEC, why cosine similarity, why compensation necessary)
2. The source/target domain distinction is unclear, as noted in the review
3. The experimental results show inconsistent performance:
- Strong results on CUB-200 and Stanford Dogs
- Weaker performance on MIT Indoor-67
- Performance degradation with limited data (Caltech 256-30)
4. Limited analysis of method's limitations and applicability conditions
5. While the method shows promise, claiming it meets "almost all requirements" overstates its completeness and generalizability given these limitations


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

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

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

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

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

The claim to be assessed is:

This meets almost all the requirements of transfer learning:

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. The existing methods can be sorted into 4 categories (Tan et al., 2018): Instances-based, mapping-based, adversarial-based, and network-based deep transfer learning. Instances-based deep transfer learning aims to reuse the instances of source domain. For example, TrAdaBoost (Dai Wenyuan et al., 2007) weights the instances of source domain to make the distribution similar to that of target domain, and perform model training based on the weighted instances from source domain along with those from target domain. 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. A representative work in this direction is Transfer Component Analysis (Pan et al., 2010), and Tzeng et al. (Tzeng et al., 2014) extents it to deep learning based solutions. 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 in feature learning, that is, optimization on transferred feature representation is driven by not only classification but also removing the gap between source and target domain. 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. An early work for speech recognition (Huang et al., 2013) partitions the neural networks of a solution into two parts: Language-irrelevant and language relevant, and reuse the language-irrelevant part for other recognition tasks. Oquab et al. (Oquab et al., 2014) reuses the Convolutional Neural Network (CNN) pre-trained on ImageNet with additional fully connected layers to adapt to target-domain classification. Another classical method, namely fine tune (Yosinski et al., 2014), reuses the original network without any change on the structure and just modifies the parameter values with the data from target domain, where fine tune can be performed on the whole network, or a part of them after fixing the other part.

Head2Toe (Evci et al., 2022) makes use of the representations from all layers and applies Lasso to obtain feature selection in their transfer learning framework.

Hereafter, AdaFilter (Guo et al., 2020) and TransTailor (Liu et al., 2021) alleviate this by reorganizing the network architecture.

AdaFilter applies Layer-wise Recurrent Gated Network to select reusable filters in original solution to form a new pipeline as target-domain solution. TransTailor introduces pruning to make network structure as concise as enough for target domain, which leads to a optimal sub-model.

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. If its position is closer to an existing cluster center, in general, a higher probability of correct classification can be expected due to the explicit association with known data representatives. In such a case, the embedding should be changed little. On the contrary, the embedding should be remedied remarkably if its association to any cluster center is not so deterministic. 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.

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

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

Here, we make use of the algorithm referred to as Deep Embedded Clustering (DEC) (Xie et al., 2016) to obtain such anchor points, and the experiments confirm that DEC performs very well in the proposed framework. The proposed transfer learning framework can work in 4 manners to form the following solutions:* (I) At first, fine tune the backbone network, and then fix the backbone and train the transfer network only.

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

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

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

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

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

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

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

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

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

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

**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 Caltech 256-30 data, only 30 data samples are randomly selected per class for training. This degrades our method much because we use clustering to select representatives but the random selection prevents our method from finding the truly representative examples due to the too sparse random sampling of 30 data examples per class in contrast to the 30,607 images in total. To verify this issue, we reestablish our solution (I) by training the clustering model with all the data samples other than the testing samples, which is referred to as (I+) in Table 1.

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

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

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

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

We propose a new transfer learning method with context-aware compensation to feature embedding, where a contextual descriptor figuring out how close from the input to each cluster center is applied, and soft gating is used to make the feature transfer focused on the data samples that truly need transferring. The experiments demonstrate that the proposed feature transferring scheme is technically sound, and a big performance improvement is achieved using a tiny network.

* Liu et al. (2021) Bingyan Liu, Yifeng Cai, Yao Guo, and Xiangqun Chen. Transtailor: Pruning the pre-trained model for improved transfer learning. In _Proceedings of the AAAI Conference on Artificial Intelligence_, volume 35, pp. 8627-8634, 2021.

* Oquab et al. (2014) Maxime Oquab, Leon Bottou, Ivan Laptev, and Josef Sivic. Learning and transferring mid-level image representations using convolutional neural networks. In _Proceedings of the IEEE conference on computer vision and pattern recognition_, pp. 1717-1724, 2014. \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 evidence and reviewer comments, I find several issues with the claim that "this meets almost all the requirements of transfer learning":

1. The reviewer points out significant weaknesses including questionable effectiveness across different backbones (only works well with ResNet-101), unclear justification of the clustering approach, and lack of analysis demonstrating why the method works.

2. The empirical results in Table 1 show mixed performance - while there are some improvements with ResNet-101, the method performs poorly with VGG16 and has inconsistent results with ResNet-50.

3. The reviewer notes that the method introduces additional computational complexity and training time that isn't properly acknowledged.

4. The paper seems to lack critical experimental validation of key components and proper comparison of computational requirements.

<score>4</score>

The claim appears significantly overstated given these limitations and the mixed empirical evidence. While the method shows some promise, claiming it meets "almost all requirements" is not well supported by the available evidence.


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

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

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

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

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

The claim to be assessed is:

This meets almost all the requirements of transfer learning:

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. The existing methods can be sorted into 4 categories (Tan et al., 2018): Instances-based, mapping-based, adversarial-based, and network-based deep transfer learning. Instances-based deep transfer learning aims to reuse the instances of source domain. For example, TrAdaBoost (Dai Wenyuan et al., 2007) weights the instances of source domain to make the distribution similar to that of target domain, and perform model training based on the weighted instances from source domain along with those from target domain. 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. A representative work in this direction is Transfer Component Analysis (Pan et al., 2010), and Tzeng et al. (Tzeng et al., 2014) extents it to deep learning based solutions. 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 in feature learning, that is, optimization on transferred feature representation is driven by not only classification but also removing the gap between source and target domain. 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. An early work for speech recognition (Huang et al., 2013) partitions the neural networks of a solution into two parts: Language-irrelevant and language relevant, and reuse the language-irrelevant part for other recognition tasks. Oquab et al. (Oquab et al., 2014) reuses the Convolutional Neural Network (CNN) pre-trained on ImageNet with additional fully connected layers to adapt to target-domain classification. Another classical method, namely fine tune (Yosinski et al., 2014), reuses the original network without any change on the structure and just modifies the parameter values with the data from target domain, where fine tune can be performed on the whole network, or a part of them after fixing the other part.

Head2Toe (Evci et al., 2022) makes use of the representations from all layers and applies Lasso to obtain feature selection in their transfer learning framework.

Hereafter, AdaFilter (Guo et al., 2020) and TransTailor (Liu et al., 2021) alleviate this by reorganizing the network architecture.

AdaFilter applies Layer-wise Recurrent Gated Network to select reusable filters in original solution to form a new pipeline as target-domain solution. TransTailor introduces pruning to make network structure as concise as enough for target domain, which leads to a optimal sub-model.

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. If its position is closer to an existing cluster center, in general, a higher probability of correct classification can be expected due to the explicit association with known data representatives. In such a case, the embedding should be changed little. On the contrary, the embedding should be remedied remarkably if its association to any cluster center is not so deterministic. 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.

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

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

Here, we make use of the algorithm referred to as Deep Embedded Clustering (DEC) (Xie et al., 2016) to obtain such anchor points, and the experiments confirm that DEC performs very well in the proposed framework. The proposed transfer learning framework can work in 4 manners to form the following solutions:* (I) At first, fine tune the backbone network, and then fix the backbone and train the transfer network only.

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

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

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

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

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

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

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

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

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

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

**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 Caltech 256-30 data, only 30 data samples are randomly selected per class for training. This degrades our method much because we use clustering to select representatives but the random selection prevents our method from finding the truly representative examples due to the too sparse random sampling of 30 data examples per class in contrast to the 30,607 images in total. To verify this issue, we reestablish our solution (I) by training the clustering model with all the data samples other than the testing samples, which is referred to as (I+) in Table 1.

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

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

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

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

We propose a new transfer learning method with context-aware compensation to feature embedding, where a contextual descriptor figuring out how close from the input to each cluster center is applied, and soft gating is used to make the feature transfer focused on the data samples that truly need transferring. The experiments demonstrate that the proposed feature transferring scheme is technically sound, and a big performance improvement is achieved using a tiny network.

* Liu et al. (2021) Bingyan Liu, Yifeng Cai, Yao Guo, and Xiangqun Chen. Transtailor: Pruning the pre-trained model for improved transfer learning. In _Proceedings of the AAAI Conference on Artificial Intelligence_, volume 35, pp. 8627-8634, 2021.

* Oquab et al. (2014) Maxime Oquab, Leon Bottou, Ivan Laptev, and Josef Sivic. Learning and transferring mid-level image representations using convolutional neural networks. In _Proceedings of the IEEE conference on computer vision and pattern recognition_, pp. 1717-1724, 2014. \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 the review comment and evidence provided, I'll assess the claim "This meets almost all the requirements of transfer learning."

The evidence shows the method incorporates several key transfer learning elements:
- Uses pre-trained backbone networks
- Applies feature compensation mechanisms
- Considers domain adaptation through clustering
- Shows competitive results on multiple datasets

However, the review raises valid concerns:
- Limited explanation of data center relationship to transfer learning
- Assumptions about classification tasks only
- Results show minimal improvements over basic fine-tuning
- Many fundamental questions about the method's workings remain unanswered

While the method does incorporate transfer learning components, claiming it meets "almost all requirements" is an overstatement given these limitations and unclear theoretical foundations.

<score>4</score>