1 citations · 2 across the 11 of their papers we have counts for
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Optimized Gradient Clipping for Noisy Label Learning
Xichen Ye, Yifan Wu, Weizhong Zhang +3
Previous research has shown that constraining the gradient of loss function with respect to model-predicted probabilities can enhance the model robustness against noisy labels. The…
Revisiting Energy-Based Model for Out-of-Distribution Detection
Yifan Wu, Xichen Ye, Songmin Dai +4
Out-of-distribution (OOD) detection is an essential approach to robustifying deep learning models, enabling them to identify inputs that fall outside of their trained distribution.…
Active Negative Loss: A Robust Framework for Learning with Noisy Labels
Xichen Ye, Yifan Wu, Yiqi Wang +3
Deep supervised learning has achieved remarkable success across a wide range of tasks, yet it remains susceptible to overfitting when confronted with noisy labels. To address this…
DuPL: Dual Student with Trustworthy Progressive Learning for Robust Weakly Supervised Semantic Segmentation
Yuanchen Wu, Xichen Ye, Kequan Yang +2
Recently, One-stage Weakly Supervised Semantic Segmentation (WSSS) with image-level labels has gained increasing interest due to simplification over its cumbersome multi-stage coun…