# A comment on Guo et al. [arXiv:2206.11228]

arXiv: https://arxiv.org/abs/2208.01456 · HTML: https://mlsanity.com/paper/2208.01456/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 2 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [Partial success in closing the gap between human and machine vision](https://mlsanity.com/paper/2106.07411/) (2021)
- [Training robust neural networks using Lipschitz bounds](https://mlsanity.com/paper/2005.02929/) (2020)
- [Adversarial Examples that Fool both Computer Vision and Time-Limited Humans](https://mlsanity.com/paper/1802.08195/) (2018)
- [Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models](https://mlsanity.com/paper/1905.05186/) (2019)
- [Adversarial Robustness through the Lens of Convolutional Filters](https://mlsanity.com/paper/2204.02481/) (2022)
- [Adversarially trained neural representations may already be as robust as corresponding biological neural representations](https://mlsanity.com/paper/2206.11228/) (2022)

## 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.