1 citations · 2 across the 17 of their papers we have counts for
5 papers · 1 filter
Class-frequency Guided Noise Schedule for Diffusion Models
Jiequan Cui, Beier Zhu, Qingshan Xu +3
In this paper, we are the first to examine the correlations between class frequency and the multi-scale noise schedule within diffusion models. For score-based generative models, l…
Reducing Class-Wise Performance Disparity via Margin Regularization
Beier Zhu, Kesen Zhao, Jiequan Cui +4
Deep neural networks often exhibit substantial disparities in class-wise accuracy, even when trained on class-balanced data, posing concerns for reliable deployment. While prior ef…
Generative Distribution Distillation
Jiequan Cui, Beier Zhu, Qingshan Xu +6
In this paper, we formulate the knowledge distillation (KD) as a conditional generative problem and propose the \textit{Generative Distribution Distillation (GenDD)} framework. A n…
Generalized Kullback-Leibler Divergence Loss
Jiequan Cui, Beier Zhu, Qingshan Xu +5
In this paper, we delve deeper into the Kullback-Leibler (KL) Divergence loss and mathematically prove that it is equivalent to the Decoupled Kullback-Leibler (DKL) Divergence loss…
Classes Are Not Equal: An Empirical Study on Image Recognition Fairness
Jiequan Cui, Beier Zhu, Xin Wen +3
In this paper, we present an empirical study on image recognition fairness, i.e., extreme class accuracy disparity on balanced data like ImageNet. We experimentally demonstrate tha…