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Better Diffusion Models Further Improve Adversarial Training. (arXiv:2302.04638v1 [cs.CV])
cs.CR updates on arXiv.org arxiv.org
It has been recognized that the data generated by the denoising diffusion
probabilistic model (DDPM) improves adversarial training. After two years of
rapid development in diffusion models, a question naturally arises: can better
diffusion models further improve adversarial training? This paper gives an
affirmative answer by employing the most recent diffusion model which has
higher efficiency ($\sim 20$ sampling steps) and image quality (lower FID
score) compared with DDPM. Our adversarially trained models achieve
state-of-the-art performance on RobustBench using only …
adversarial data development diffusion models efficiency generated higher quality question rapid score sim training