Pay Attention to Features, Transfer Learn faster CNNs
Deep convolutional neural networks are now widely deployed in vision applications, but the size of training data can bottleneck their performance. Transfer learning offers the chance for CNNs to learn with limited data samples by transferring knowledge from weights pretrained on large datasets. On the other hand, blindly transferring all learned features from the source dataset brings unnecessary computation to CNNs on the target task. In this paper, we propose attentive feature distillation and selection (AFDS) that not only adjusts the strength of regularization introduced by transfer learning but also dynamically determines which are the important features to transfer. When deploying AFDS on ResNet-101, we achieve state-of-the-art computation reduction at the same accuracy budget, outperforming all existing transfer learning methods. On a 10x MACs reduction budget, transfer learned from ImageNet to Stanford Dogs 120, AFDS achieves an accuracy that is 12.51% higher than its best competitor.
In general, I think it is a good paper and I like the contribution of the author. I think they explain in detail the methodology. The results compare the new methodologies with different databases which increase the credibility of the results. However, there is a couple of additional question that is important to manage: 1) The paper presents three different contributions. However, it is so clear how this work helps for *By changing the fraction of channel neurons to skip for each convolution, AFDS can further accelerate the transfer learned models while minimizing the impact on task accuracy* I think a better explanation of this part it would be necessary. 2) The comparison of the results are very focused on AFDS, Did you compare the results with different transfer learning approach? 3) During the training procedure. We need a better explanation of why *we found that in residual networks with greater depths, AFS could become notably challenging to train to high accuracies*. Also, the results of the empirical test it would be useful to understand the challenges to train the network. 4) I think it would be useful to have the code available for the final version.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Pay Attention to Features, Transfer Learn faster CNNs
Deep convolutional neural networks are now widely deployed in vision applications, but the size of training data can bottleneck their performance. Transfer learning offers the chance for CNNs to learn with limited data samples by transferring knowledge from weights pretrained on large datasets. On the other hand, blindly transferring all learned features from the source dataset brings unnecessary computation to CNNs on the target task. In this paper, we propose attentive feature distillation and selection (AFDS) that not only adjusts the strength of regularization introduced by transfer learning but also dynamically determines which are the important features to transfer. When deploying AFDS on ResNet-101, we achieve state-of-the-art computation reduction at the same accuracy budget, outperforming all existing transfer learning methods. On a 10x MACs reduction budget, transfer learned from ImageNet to Stanford Dogs 120, AFDS achieves an accuracy that is 12.51% higher than its best competitor.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



In general, I think it is a good paper and I like the contribution of the author. I think they explain in detail the methodology. The results compare the new methodologies with different databases which increase the credibility of the results. However, there is a couple of additional question that is important to manage: 1) The paper presents three different contributions. However, it is so clear how this work helps for *By changing the fraction of channel neurons to skip for each convolution, AFDS can further accelerate the transfer learned models while minimizing the impact on task accuracy* I think a better explanation of this part it would be necessary. 2) The comparison of the results are very focused on AFDS, Did you compare the results with different transfer learning approach? 3) During the training procedure. We need a better explanation of why *we found that in residual networks with greater depths, AFS could become notably challenging to train to high accuracies*. Also, the results of the empirical test it would be useful to understand the challenges to train the network. 4) I think it would be useful to have the code available for the final version.
assistant
In general, I think it is a good paper and I like the contribution of the author. I think they explain in detail the methodology. The results compare the new methodologies with different databases which increase the credibility of the results. However, there is a couple of additional question that is important to manage: 1) The paper presents three different contributions. However, it is so clear how this work helps for *By changing the fraction of channel neurons to skip for each convolution, AFDS can further accelerate the transfer learned models while minimizing the impact on task accuracy* I think a better explanation of this part it would be necessary. 2) The comparison of the results are very focused on AFDS, Did you compare the results with different transfer learning approach? 3) During the training procedure. We need a better explanation of why *we found that in residual networks with greater depths, AFS could become notably challenging to train to high accuracies*. Also, the results of the empirical test it would be useful to understand the challenges to train the network. 4) I think it would be useful to have the code available for the final version.