9 papers
GAMR: Geometric-Aware Manifold Regularization with Virtual Outlier Synthesis for Learning with Noisy Labels
Ningkang Peng, Jingyang Mao, Xiaoqian Peng +4
Deep neural networks (DNNs) experience significant performance degradation when processing noisy labels, primarily due to overfitting on mislabeled data. Current mainstream approac…
Radial-Angular Geometry for Reliable Update Diagnosis in Noisy-Label Learning
Ningkang Peng, Jingyang Mao, Xiaoqian Peng +2
Noisy-label methods often estimate sample reliability from forward-space signals such as loss, confidence, or entropy. These signals indicate whether a sample is difficult to predi…
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…
Don't Break the Boundary: Continual Unlearning for OOD Detection Based on Free Energy Repulsion
Ningkang Peng, Kun Shao, Jingyang Mao +4
Deploying trustworthy AI in open-world environments faces a dual challenge: the necessity for robust Out-of-Distribution (OOD) detection to ensure system safety, and the demand for…
Halt the Hallucination: Decoupling Signal and Semantic OOD Detection Based on Cascaded Early Rejection
Ningkang Peng, Chuanjie Cheng, Jingyang Mao +7
Efficient and robust Out-of-Distribution (OOD) detection is paramount for safety-critical applications.However, existing methods still execute full-scale inference on low-level sta…