3 citations · 5 across the 5 of their papers we have counts for
5 papers
Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget
Vikash Sehwag, Xianghao Kong, Jingtao Li +2
As scaling laws in generative AI push performance, they also simultaneously concentrate the development of these models among actors with large computational resources. With a focu…
Your Diffusion Model is Secretly a Noise Classifier and Benefits from Contrastive Training
Yunshu Wu, Yingtao Luo, Xianghao Kong +2
Diffusion models learn to denoise data and the trained denoiser is then used to generate new samples from the data distribution. In this paper, we revisit the diffusion sampling pr…
Asymmetric Bias in Text-to-Image Generation with Adversarial Attacks
Haz Sameen Shahgir, Xianghao Kong, Greg Ver Steeg +1
The widespread use of Text-to-Image (T2I) models in content generation requires careful examination of their safety, including their robustness to adversarial attacks. Despite exte…
Interpretable Diffusion via Information Decomposition
Xianghao Kong, Ollie Liu, Han Li +2
Denoising diffusion models enable conditional generation and density modeling of complex relationships like images and text. However, the nature of the learned relationships is opa…
Information-Theoretic Diffusion
Xianghao Kong, Rob Brekelmans, Greg Ver Steeg
Denoising diffusion models have spurred significant gains in density modeling and image generation, precipitating an industrial revolution in text-guided AI art generation. We intr…