Out-of-distribution (OOD) detection is a critical task for reliable machine
learning. Recent advances in representation learning give rise to
distance-based OOD detection, where testing samples are detected as OOD if they
are relatively far away from the centroids or prototypes of in-distribution
(ID) classes. However, prior methods directly take off-the-shelf contrastive
losses that suffice for classifying ID samples, but are not optimally designed
when test inputs contain OOD samples. In this work, we propose CIDER, a novel
representation learning framework that exploits hyperspherical embeddings for
OOD detection. CIDER jointly optimizes two losses to promote strong ID-OOD
separability: a dispersion loss that promotes large angular distances among
different class prototypes, and a compactness loss that encourages samples to
be close to their class prototypes. We analyze and establish the unexplored
relationship between OOD detection performance and the embedding properties in
the hyperspherical space, and demonstrate the importance of dispersion and
compactness. CIDER establishes superior performance, outperforming the latest
rival by 19.36% in FPR95. Code is available at
this https URL

提出了一种基于超球嵌入的新型表示学习框架 CIDER，该框架共同优化两个损失，以促进强的 ID-OOD 可分离性，并通过分析在超球空间中嵌入特性与 OOD 检测性能之间的关系，演示了离散度和紧凑性的重要性，结果表明 CIDER 表现优异，FPR95 比最新对手高出 19.36 %。