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Generalization of Diffusion Models Arises with a Balanced Representation Space
Zekai Zhang, Xiao Li, Xiang Li +4
Diffusion models excel at generating high-quality, diverse samples, yet they risk memorizing training data when overfit to the training objective. We analyze the distinctions betwe…
A Closer Look at Model Collapse: From a Generalization-to-Memorization Perspective
Lianghe Shi, Meng Wu, Huijie Zhang +3
The widespread use of diffusion models has led to an abundance of AI-generated data, raising concerns about model collapse -- a phenomenon in which recursive iterations of training…
Understanding Generalization in Diffusion Distillation via Probability Flow Distance
Huijie Zhang, Zijian Huang, Siyi Chen +4
Diffusion distillation provides an effective approach for learning lightweight and few-steps diffusion models with efficient generation. However, evaluating their generalization re…
Understanding Representation Dynamics of Diffusion Models via Low-Dimensional Modeling
Xiao Li, Zekai Zhang, Xiang Li +4
Diffusion models, though originally designed for generative tasks, have demonstrated impressive self-supervised representation learning capabilities. A particularly intriguing phen…
Breaking the Curse of Dimensionality: Diffusion Models Efficiently Learn Low-Dimensional Distributions
Peng Wang, Huijie Zhang, Zekai Zhang +3
Despite their empirical success across a wide range of generative tasks, the fundamental principles underlying the ability of diffusion models to learn data distributions are poorl…
Efficient Compression of Overparameterized Deep Models through Low-Dimensional Learning Dynamics
Soo Min Kwon, Zekai Zhang, Dogyoon Song +2
Overparameterized models have proven to be powerful tools for solving various machine learning tasks. However, overparameterization often leads to a substantial increase in computa…