Feature prioritization and regularization improve standard accuracy and adversarial robustness
Adversarial training has been successfully applied to build robust models at a certain cost. While the robustness of a model increases, the standard classification accuracy declines. This phenomenon is suggested to be an inherent trade-off. We propose a model that employs feature prioritization by a nonlinear attention module and feature regularization to improve the adversarial robustness and the standard accuracy relative to adversarial training. The attention module encourages the model to rely heavily on robust features by assigning larger weights to them while suppressing non-robust features. The regularizer encourages the model to extracts similar features for the natural and adversarial images, effectively ignoring the added perturbation. In addition to evaluating the robustness of our model, we provide justification for the attention module and propose a novel experimental strategy that quantitatively demonstrates that our model is almost ideally aligned with salient data characteristics. Additional experimental results illustrate the power of our model relative to the state of the art methods.
 This paper proposes a new architecture for adversarial training that is able to improve both accuracy and robustness performances using an attention-based model for feature prioritization and L2 regularization as implicit denoising. The paper is very clear and well written and the contribution is relevant to ICLR. Pros: - The background, model and experiments are clearly explained. The paper provides fair comparisons with a strong baseline on standard datasets. - Using attention mechanisms to improve the model robustness in an adversarial training setting is a strong and novel contribution - Both quantitative and qualitative results are interesting.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Feature prioritization and regularization improve standard accuracy and adversarial robustness
Adversarial training has been successfully applied to build robust models at a certain cost. While the robustness of a model increases, the standard classification accuracy declines. This phenomenon is suggested to be an inherent trade-off. We propose a model that employs feature prioritization by a nonlinear attention module and feature regularization to improve the adversarial robustness and the standard accuracy relative to adversarial training. The attention module encourages the model to rely heavily on robust features by assigning larger weights to them while suppressing non-robust features. The regularizer encourages the model to extracts similar features for the natural and adversarial images, effectively ignoring the added perturbation. In addition to evaluating the robustness of our model, we provide justification for the attention module and propose a novel experimental strategy that quantitatively demonstrates that our model is almost ideally aligned with salient data characteristics. Additional experimental results illustrate the power of our model relative to the state of the art methods.
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>



 This paper proposes a new architecture for adversarial training that is able to improve both accuracy and robustness performances using an attention-based model for feature prioritization and L2 regularization as implicit denoising. The paper is very clear and well written and the contribution is relevant to ICLR. Pros: - The background, model and experiments are clearly explained. The paper provides fair comparisons with a strong baseline on standard datasets. - Using attention mechanisms to improve the model robustness in an adversarial training setting is a strong and novel contribution - Both quantitative and qualitative results are interesting.
assistant
 This paper proposes a new architecture for adversarial training that is able to improve both accuracy and robustness performances using an attention-based model for feature prioritization and L2 regularization as implicit denoising. The paper is very clear and well written and the contribution is relevant to ICLR. Pros: - The background, model and experiments are clearly explained. The paper provides fair comparisons with a strong baseline on standard datasets. - Using attention mechanisms to improve the model robustness in an adversarial training setting is a strong and novel contribution - Both quantitative and qualitative results are interesting.