29 citations · 45 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 8 cited
Toward Understanding Generative Data Augmentation
Chenyu Zheng, Guoqiang Wu, Chongxuan Li
Generative data augmentation, which scales datasets by obtaining fake labeled examples from a trained conditional generative model, boosts classification performance in various lea…
cs.LG2023★ 29 cited
One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale
Fan Bao, Shen Nie, Kaiwen Xue +7
This paper proposes a unified diffusion framework (dubbed UniDiffuser) to fit all distributions relevant to a set of multi-modal data in one model. Our key insight is -- learning d…
cs.LG2023★ 8 cited
Revisiting Discriminative vs. Generative Classifiers: Theory and Implications
Chenyu Zheng, Guoqiang Wu, Fan Bao +3
A large-scale deep model pre-trained on massive labeled or unlabeled data transfers well to downstream tasks. Linear evaluation freezes parameters in the pre-trained model and trai…