Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Understanding the Generative Capability of Adversarially Robust
  Classifiers   Recently, some works found an interesting phenomenon that adversarially
robust classifiers can generate good images comparable to generative models. We
investigate this phenomenon from an energy perspective and provide a novel
explanation. We reformulate adversarial example generation, adversarial
training, and image generation in terms of an energy function. We find that
adversarial training contributes to obtaining an energy function that is flat
and has low energy around the real data, which is the key for generative
capability. Based on our new understanding, we further propose a better
adversarial training method, Joint Energy Adversarial Training (JEAT), which
can generate high-quality images and achieve new state-of-the-art robustness
under a wide range of attacks. The Inception Score of the images (CIFAR-10)
generated by JEAT is 8.80, much better than original robust classifiers (7.50).

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The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
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The categories of this paper are: comp-gas
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc