A Rate-Distortion Theory of Adversarial Examples
The generalization ability of deep neural networks (DNNs) is intertwined with model complexity, robustness, and capacity. Through establishing an equivalence between a DNN and a noisy communication channel, we characterize generalization and fault tolerance for unbounded adversarial attacks in terms of information-theoretic quantities. Invoking rate-distortion theory, we suggest that excess capacity is a significant cause of vulnerability to adversarial examples.
 This paper considers the trade-off between the prediction accuracy of deep neural networks (DNNs) and sensitivity to adversarial examples. Reviewing the (Gaussian) channel capacity and rate-distortion theory, i.e., the information bottleneck, the authors discuss their implications on the generalization performance of DNNs. The experiments demonstrate the SNR of gradients, information plane, the generalization gap, and fault tolerance against adversarial examples. While the interpretations of DNN learning by the information theoretic concepts are interesting, most of them are already known results, and hence provide little novel theoretical knowledge. The discussions in Section 3 are superficial. It is not clear how they are related to the main arguments of this paper. While the experiment of fault tolerance is interesting, the implications obtained from experiments are somewhat trivial. minor comments: p.2, l.15: h, w, and c are undefined. Section 2: Rate-distortion theory is usually explained by the sphere covering argument instead of sphere packing. Section 4.3.1: It is not explained what zero and one-shot transfer learning is. Pros: The experiment of fault tolerance is interesting. Cons: Theoretical parts are basic results of information theory. The implications of experiments are somewhat trivial.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
A Rate-Distortion Theory of Adversarial Examples
The generalization ability of deep neural networks (DNNs) is intertwined with model complexity, robustness, and capacity. Through establishing an equivalence between a DNN and a noisy communication channel, we characterize generalization and fault tolerance for unbounded adversarial attacks in terms of information-theoretic quantities. Invoking rate-distortion theory, we suggest that excess capacity is a significant cause of vulnerability to adversarial examples.
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 considers the trade-off between the prediction accuracy of deep neural networks (DNNs) and sensitivity to adversarial examples. Reviewing the (Gaussian) channel capacity and rate-distortion theory, i.e., the information bottleneck, the authors discuss their implications on the generalization performance of DNNs. The experiments demonstrate the SNR of gradients, information plane, the generalization gap, and fault tolerance against adversarial examples. While the interpretations of DNN learning by the information theoretic concepts are interesting, most of them are already known results, and hence provide little novel theoretical knowledge. The discussions in Section 3 are superficial. It is not clear how they are related to the main arguments of this paper. While the experiment of fault tolerance is interesting, the implications obtained from experiments are somewhat trivial. minor comments: p.2, l.15: h, w, and c are undefined. Section 2: Rate-distortion theory is usually explained by the sphere covering argument instead of sphere packing. Section 4.3.1: It is not explained what zero and one-shot transfer learning is. Pros: The experiment of fault tolerance is interesting. Cons: Theoretical parts are basic results of information theory. The implications of experiments are somewhat trivial.
assistant
 This paper considers the trade-off between the prediction accuracy of deep neural networks (DNNs) and sensitivity to adversarial examples. Reviewing the (Gaussian) channel capacity and rate-distortion theory, i.e., the information bottleneck, the authors discuss their implications on the generalization performance of DNNs. The experiments demonstrate the SNR of gradients, information plane, the generalization gap, and fault tolerance against adversarial examples. While the interpretations of DNN learning by the information theoretic concepts are interesting, most of them are already known results, and hence provide little novel theoretical knowledge. The discussions in Section 3 are superficial. It is not clear how they are related to the main arguments of this paper. While the experiment of fault tolerance is interesting, the implications obtained from experiments are somewhat trivial. minor comments: p.2, l.15: h, w, and c are undefined. Section 2: Rate-distortion theory is usually explained by the sphere covering argument instead of sphere packing. Section 4.3.1: It is not explained what zero and one-shot transfer learning is. Pros: The experiment of fault tolerance is interesting. Cons: Theoretical parts are basic results of information theory. The implications of experiments are somewhat trivial.