Robustness May Be at Odds with Accuracy
We show that there exists an inherent tension between the goal of adversarial robustness and that of standard generalization. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists even in a fairly simple and natural setting. These findings also corroborate a similar phenomenon observed in practice. Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. These differences, in particular, seem to result in unexpected benefits: the features learned by robust models tend to align better with salient data characteristics and human perception.
 The paper demonstrates the trade-off between accuracy and robustness of a model. The phenomenon is shown in previous works, but this work interestingly proposes a theoretical model that supports the idea. The proving technique can be particularly beneficial to developing theoretical understanding for the phenomenon. Besides, the authors also visualize the gradients and adversarial examples generated from standard and adversarially trained models, which show that these adversarially trained models are more aligned to human perception. Quality: good, clarity: good, originality: good, significance: good Pros: - The paper is fairly well written and the idea is clearly presented - To the best of my knowledge (maye I am wrong), this work is the first one that provides theoretical explanation for the tradeoff between accuracy and robustness - The visualization results supports their hypothesis that adversarially trained models percepts more like human. Suggestions: It would be interesting to see what kind of real images can fool the models and see whether the robust model made mistakes more like human.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Robustness May Be at Odds with Accuracy
We show that there exists an inherent tension between the goal of adversarial robustness and that of standard generalization. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists even in a fairly simple and natural setting. These findings also corroborate a similar phenomenon observed in practice. Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. These differences, in particular, seem to result in unexpected benefits: the features learned by robust models tend to align better with salient data characteristics and human perception.
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 demonstrates the trade-off between accuracy and robustness of a model. The phenomenon is shown in previous works, but this work interestingly proposes a theoretical model that supports the idea. The proving technique can be particularly beneficial to developing theoretical understanding for the phenomenon. Besides, the authors also visualize the gradients and adversarial examples generated from standard and adversarially trained models, which show that these adversarially trained models are more aligned to human perception. Quality: good, clarity: good, originality: good, significance: good Pros: - The paper is fairly well written and the idea is clearly presented - To the best of my knowledge (maye I am wrong), this work is the first one that provides theoretical explanation for the tradeoff between accuracy and robustness - The visualization results supports their hypothesis that adversarially trained models percepts more like human. Suggestions: It would be interesting to see what kind of real images can fool the models and see whether the robust model made mistakes more like human.
assistant
 The paper demonstrates the trade-off between accuracy and robustness of a model. The phenomenon is shown in previous works, but this work interestingly proposes a theoretical model that supports the idea. The proving technique can be particularly beneficial to developing theoretical understanding for the phenomenon. Besides, the authors also visualize the gradients and adversarial examples generated from standard and adversarially trained models, which show that these adversarially trained models are more aligned to human perception. Quality: good, clarity: good, originality: good, significance: good Pros: - The paper is fairly well written and the idea is clearly presented - To the best of my knowledge (maye I am wrong), this work is the first one that provides theoretical explanation for the tradeoff between accuracy and robustness - The visualization results supports their hypothesis that adversarially trained models percepts more like human. Suggestions: It would be interesting to see what kind of real images can fool the models and see whether the robust model made mistakes more like human.