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 informationtheoretic 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.