CEB Improves Model Robustness
We demonstrate that the Conditional Entropy Bottleneck (CEB) can improve model robustness. CEB is an easy strategy to implement and works in tandem with data augmentation procedures. We report results of a large scale adversarial robustness study on CIFAR-10, as well as the IMAGENET-C Common Corruptions Benchmark.
TITLE CEB Improves Model Robustness REVIEW SUMMARY As a thorough empirical evaluation of a new method for training deep neural networks, this paper is a useful contribution. PAPER SUMMARY The paper presents an empirical study of the robustness of neural network classifiers trained using a conditional entropy bottleneck regularization. CLARITY The paper is well written and easy to follow. Results are presented in a way that gives the reader a good overview, but figure quality / legibility could be improved. ORIGINALITY The CEB method is an original (but minor) modification of a well known method. SIGNIFICANCE The primary contribution of the paper is a thorough experimental evaluation of the particular regularization method presented in the paper. While this is certainly useful, it would have been even better with more comparison with other regularization methods. The paper clearly demonstrates the benefits of CEB, which is of interest in itself. FURTHER COMMENTS Consider making a more clear distinction between generalization and robustness. *are are* Double word. *all machine-learned systems...highly vunerable* Too strong statement. *VIB* Abbreviation not defined in the text. Figure 1, 2 and 3 are a bit hard to read (especially the dotted lines)
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
CEB Improves Model Robustness
We demonstrate that the Conditional Entropy Bottleneck (CEB) can improve model robustness. CEB is an easy strategy to implement and works in tandem with data augmentation procedures. We report results of a large scale adversarial robustness study on CIFAR-10, as well as the IMAGENET-C Common Corruptions Benchmark.
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>



TITLE CEB Improves Model Robustness REVIEW SUMMARY As a thorough empirical evaluation of a new method for training deep neural networks, this paper is a useful contribution. PAPER SUMMARY The paper presents an empirical study of the robustness of neural network classifiers trained using a conditional entropy bottleneck regularization. CLARITY The paper is well written and easy to follow. Results are presented in a way that gives the reader a good overview, but figure quality / legibility could be improved. ORIGINALITY The CEB method is an original (but minor) modification of a well known method. SIGNIFICANCE The primary contribution of the paper is a thorough experimental evaluation of the particular regularization method presented in the paper. While this is certainly useful, it would have been even better with more comparison with other regularization methods. The paper clearly demonstrates the benefits of CEB, which is of interest in itself. FURTHER COMMENTS Consider making a more clear distinction between generalization and robustness. *are are* Double word. *all machine-learned systems...highly vunerable* Too strong statement. *VIB* Abbreviation not defined in the text. Figure 1, 2 and 3 are a bit hard to read (especially the dotted lines)
assistant
TITLE CEB Improves Model Robustness REVIEW SUMMARY As a thorough empirical evaluation of a new method for training deep neural networks, this paper is a useful contribution. PAPER SUMMARY The paper presents an empirical study of the robustness of neural network classifiers trained using a conditional entropy bottleneck regularization. CLARITY The paper is well written and easy to follow. Results are presented in a way that gives the reader a good overview, but figure quality / legibility could be improved. ORIGINALITY The CEB method is an original (but minor) modification of a well known method. SIGNIFICANCE The primary contribution of the paper is a thorough experimental evaluation of the particular regularization method presented in the paper. While this is certainly useful, it would have been even better with more comparison with other regularization methods. The paper clearly demonstrates the benefits of CEB, which is of interest in itself. FURTHER COMMENTS Consider making a more clear distinction between generalization and robustness. *are are* Double word. *all machine-learned systems...highly vunerable* Too strong statement. *VIB* Abbreviation not defined in the text. Figure 1, 2 and 3 are a bit hard to read (especially the dotted lines)