3 accepted papers
Diffusion models have achieved state-of-the-art performance in image generation tasks. However, training powerful diffusion models remains time-consuming, which limits their practical deployment. In this paper, we revisit the learning dynamics of diffusion models through the lens of *spectral bias*,…
Diffusion models have recently emerged as a powerful class of generative models and have achieved state-of-the-art performance in various image synthesis tasks. However, training diffusion models generally requires large amounts of data and suffer from overfitting when the dataset size is limited.…
In recent years, there has been an explosion of research into developing robust deep neural networks against adversarial examples. As one of the most successful methods, Adversarial Training (AT) has been widely studied before, but there is still a gap to achieve promising clean and robust accuracy…