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TSRE: Channel-Aware Typical Set Refinement for Out-of-Distribution Detection
Weijun Gao, Rundong He, Jinyang Dong +1
Out-of-Distribution (OOD) detection is a critical capability for ensuring the safe deployment of machine learning models in open-world environments, where unexpected or anomalous i…
Diverse Teacher-Students for Deep Safe Semi-Supervised Learning under Class Mismatch
Qikai Wang, Rundong He, Yongshun Gong +4
Semi-supervised learning can significantly boost model performance by leveraging unlabeled data, particularly when labeled data is scarce. However, real-world unlabeled data often…
CLIP-driven Outliers Synthesis for few-shot OOD detection
Hao Sun, Rundong He, Zhongyi Han +3
Few-shot OOD detection focuses on recognizing out-of-distribution (OOD) images that belong to classes unseen during training, with the use of only a small number of labeled in-dist…
Topological Structure Learning for Weakly-Supervised Out-of-Distribution Detection
Rundong He, Rongxue Li, Zhongyi Han +1
Out-of-distribution (OOD) detection is the key to deploying models safely in the open world. For OOD detection, collecting sufficient in-distribution (ID) labeled data is usually m…