2 citations · 4 across the 9 of their papers we have counts for
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cs.CV2024★ 1 cited
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.…
cs.CV2024★ 1 cited
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…
cs.CV2023★ 2 cited
Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection
Songmin Dai, Yifan Wu, Xiaoqiang Li +1
Recent unsupervised anomaly detection methods often rely on feature extractors pretrained with auxiliary datasets or on well-crafted anomaly-simulated samples. However, this might…