Rethinking Out-of-Distribution Detection on Imbalanced Data Distribution

Detecting and rejecting unknown out-of-distribution (OOD) samples is critical for deployed neural networks to void unreliable predictions. In real-world scenarios, however, the efficacy of existing OOD detection methods is often impeded by the inherent imbalance of in-distribution (ID) data, which c...

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Hauptverfasser: Liu, Kai, Fu, Zhihang, Jin, Sheng, Chen, Chao, Chen, Ze, Jiang, Rongxin, Zhou, Fan, Chen, Yaowu, Ye, Jieping
Format: Artikel
Sprache:eng
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