Towards Understanding the Transferability of Deep Representations
Deep neural networks trained on a wide range of datasets demonstrate impressive transferability. Deep features appear general in that they are applicable to many datasets and tasks. Such property is in prevalent use in real-world applications. A neural network pretrained on large datasets, such as ImageNet, can significantly boost generalization and accelerate training if fine-tuned to a smaller target dataset. Despite its pervasiveness, few effort has been devoted to uncovering the reason of transferability in deep feature representations. This paper tries to understand transferability from the perspectives of improved generalization, optimization and the feasibility of transferability. We demonstrate that 1) Transferred models tend to find flatter minima, since their weight matrices stay close to the original flat region of pretrained parameters when transferred to a similar target dataset; 2) Transferred representations make the loss landscape more favorable with improved Lipschitzness, which accelerates and stabilizes training substantially. The improvement largely attributes to the fact that the principal component of gradient is suppressed in the pretrained parameters, thus stabilizing the magnitude of gradient in back-propagation. 3) The feasibility of transferability is related to the similarity of both input and label. And a surprising discovery is that the feasibility is also impacted by the training stages in that the transferability first increases during training, and then declines. We further provide a theoretical analysis to verify our observations.
The paper gives an extensive empirical and somewhat limited theoretical analysis for the transferability in DNNs. It is shown that transferred models tend to have flatter minima with improved Lipschitzness in the loss function when good choices of pretraining are made. - The paper is well written and well-organized. Notations and claims are clear. - This paper presents an interesting line of research, that in my opinion, would be interesting to many researchers in the field, and could attract many follow up works. - Empirical analysis in sections 3 and 4 are interesting, and give good sense of generalization and optimization landscape. Analyzing the Frobenius norm of the deviation between fine-tuned network and fixed network is reasonable. - The theoretical analysis seems like a good start, but it is not sufficient in general. There seems to be gap between the network architectures used in empirical evaluations and the theoretical results. However, analyzing transferability is an important topic that needs to be evaluated more. This paper presents interesting new steps towards that goal. That being said, I would be interested to see theoretical results for more general cases alongside experiments on different types of applications. - Overall, the paper presents a novel approach for evaluating transferability, that I think would be interesting to many researchers in this field. The theoretical results are still limited, and should be investigated more.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Towards Understanding the Transferability of Deep Representations
Deep neural networks trained on a wide range of datasets demonstrate impressive transferability. Deep features appear general in that they are applicable to many datasets and tasks. Such property is in prevalent use in real-world applications. A neural network pretrained on large datasets, such as ImageNet, can significantly boost generalization and accelerate training if fine-tuned to a smaller target dataset. Despite its pervasiveness, few effort has been devoted to uncovering the reason of transferability in deep feature representations. This paper tries to understand transferability from the perspectives of improved generalization, optimization and the feasibility of transferability. We demonstrate that 1) Transferred models tend to find flatter minima, since their weight matrices stay close to the original flat region of pretrained parameters when transferred to a similar target dataset; 2) Transferred representations make the loss landscape more favorable with improved Lipschitzness, which accelerates and stabilizes training substantially. The improvement largely attributes to the fact that the principal component of gradient is suppressed in the pretrained parameters, thus stabilizing the magnitude of gradient in back-propagation. 3) The feasibility of transferability is related to the similarity of both input and label. And a surprising discovery is that the feasibility is also impacted by the training stages in that the transferability first increases during training, and then declines. We further provide a theoretical analysis to verify our observations.
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>



The paper gives an extensive empirical and somewhat limited theoretical analysis for the transferability in DNNs. It is shown that transferred models tend to have flatter minima with improved Lipschitzness in the loss function when good choices of pretraining are made. - The paper is well written and well-organized. Notations and claims are clear. - This paper presents an interesting line of research, that in my opinion, would be interesting to many researchers in the field, and could attract many follow up works. - Empirical analysis in sections 3 and 4 are interesting, and give good sense of generalization and optimization landscape. Analyzing the Frobenius norm of the deviation between fine-tuned network and fixed network is reasonable. - The theoretical analysis seems like a good start, but it is not sufficient in general. There seems to be gap between the network architectures used in empirical evaluations and the theoretical results. However, analyzing transferability is an important topic that needs to be evaluated more. This paper presents interesting new steps towards that goal. That being said, I would be interested to see theoretical results for more general cases alongside experiments on different types of applications. - Overall, the paper presents a novel approach for evaluating transferability, that I think would be interesting to many researchers in this field. The theoretical results are still limited, and should be investigated more.
assistant
The paper gives an extensive empirical and somewhat limited theoretical analysis for the transferability in DNNs. It is shown that transferred models tend to have flatter minima with improved Lipschitzness in the loss function when good choices of pretraining are made. - The paper is well written and well-organized. Notations and claims are clear. - This paper presents an interesting line of research, that in my opinion, would be interesting to many researchers in the field, and could attract many follow up works. - Empirical analysis in sections 3 and 4 are interesting, and give good sense of generalization and optimization landscape. Analyzing the Frobenius norm of the deviation between fine-tuned network and fixed network is reasonable. - The theoretical analysis seems like a good start, but it is not sufficient in general. There seems to be gap between the network architectures used in empirical evaluations and the theoretical results. However, analyzing transferability is an important topic that needs to be evaluated more. This paper presents interesting new steps towards that goal. That being said, I would be interested to see theoretical results for more general cases alongside experiments on different types of applications. - Overall, the paper presents a novel approach for evaluating transferability, that I think would be interesting to many researchers in this field. The theoretical results are still limited, and should be investigated more.