7 papers
HamBR: Active Decision Boundary Restoration Based on Hamiltonian Dynamics for Learning with Noisy Labels
Ningkang Peng, Jingyang Mao, Qianfeng Yu +3
In large-scale visual recognition and data mining tasks, the presence of noisy labels severely undermines the generalization capability of deep neural networks (DNNs). Prevalent sa…
How to Achieve Prototypical Birth and Death for OOD Detection?
Ningkang Peng, Qianfeng Yu, Xiaoqian Peng +7
Out-of-Distribution (OOD) detection is crucial for the secure deployment of machine learning models, and prototype-based learning methods are among the mainstream strategies for ac…
PIS: A Physics-Informed System for Accurate State Partitioning of Protein Trajectories
Qianfeng Yu, Ningkang Peng, Yanhui Gu
Understanding the conformational evolution of -amyloid (), particularly the isoform, is fundamental to elucidating the pathogenic mechanisms underlying Alzheime…
VMF-GOS: Geometry-guided virtual Outlier Synthesis for Long-Tailed OOD Detection
Ningkang Peng, Qianfeng Yu, Yuhao Zhang +6
Out-of-Distribution (OOD) detection under long-tailed distributions is a highly challenging task because the scarcity of samples in tail classes leads to blurred decision boundarie…
Breaking Semantic Hegemony: Decoupling Principal and Residual Subspaces for Generalized OOD Detection
Ningkang Peng, Xiaoqian Peng, Yuhao Zhang +7
While feature-based post-hoc methods have made significant strides in Out-of-Distribution (OOD) detection, we uncover a counter-intuitive Simplicity Paradox in existing state-of-th…
Learning with Adaptive Prototype Manifolds for Out-of-Distribution Detection
Ningkang Peng, JiuTao Zhou, Yuhao Zhang +6
Out-of-distribution (OOD) detection is a critical task for the safe deployment of machine learning models in the real world. Existing prototype-based representation learning method…