A comment on Guo et al. [arXiv:2206.11228]
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Abstract (arXiv)
In a recent article, Guo et al. [arXiv:2206.11228] report that adversarially trained neural representations in deep networks may already be as robust as corresponding primate IT neural representations. While we find the paper's primary experiment illuminating, we have doubts about the interpretation and phrasing of the results presented in the paper.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 11 references that are in the map. First posted on arXiv 2 Aug 2022.
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6 of 11 references.
References · 6
- 2021Partial success in closing the gap between human and machine visioncited by 185
- 2020Training robust neural networks using Lipschitz boundscited by 98
- 2018Adversarial Examples that Fool both Computer Vision and Time-Limited Humanscited by 73
- 2019Harnessing the Vulnerability of Latent Layers in Adversarially Trained Modelscited by 26
- 2022Adversarial Robustness through the Lens of Convolutional Filterscited by 9
- 2022Adversarially trained neural representations may already be as robust as corresponding biological neural representationscited by 4
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