4 papers
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…
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…
A Multi-dimensional Semantic Surprise Framework Based on Low-Entropy Semantic Manifolds for Fine-Grained Out-of-Distribution Detection
Ningkang Peng, Yuzhe Mao, Yuhao Zhang +5
Out-of-Distribution (OOD) detection is a cornerstone for the safe deployment of AI systems in the open world. However, existing methods treat OOD detection as a binary classificati…