Gangfeng Hu — accepted AI-conference papers

2026

Variation-Bounded Loss for Noise-Tolerant Learning

Jialiang Wang, Xiong Zhou, Xianming Liu, Gangfeng Hu, Deming Zhai, Junjun Jiang +1

AAAI 2026technical

Mitigating the negative impact of noisy labels has been a perennial issue in supervised learning. Robust loss functions have emerged as a prevalent solution to this problem. In this work, we introduce the Variation Ratio as a novel property related to the robustness of loss functions, and propose a

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2025

Joint Asymmetric Loss for Learning with Noisy Labels

Jialiang Wang, Xianming Liu, Xiong Zhou, Gangfeng Hu, Deming Zhai, Junjun Jiang +1

ICCV 2025poster

Learning with noisy labels is a crucial task for training accurate deep neural networks. To mitigate label noise, prior studies have proposed various robust loss functions, particularly symmetric losses. Nevertheless, symmetric losses usually suffer from the underfitting issue due to the overly stri…

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