Enhancing out-of-distribution detection via diversified multi-prototype contrastive learning

Detecting out-of-distribution (OOD) inputs is critical for safely deploying deep neural networks in the open world. Recent distance-based contrastive learning methods demonstrated their effectiveness by learning improved feature representations in the embedding space. However, those methods might lead to an incomplete and ambiguous representation of a class, thereby resulting in the loss of intra-class semantic information. In this work, we propose a novel diversified multi-prototype contrastive learning framework, which preserves the semantic knowledge within each class's embedding space by introducing multiple fine-grained prototypes for each class. This preserves intrinsic features within the in-distribution data, promoting discrimination against OOD samples. We also devise an activation constraints technique to mitigate the impact of extreme activation values on other dimensions and facilitate the computation of distance-based scores. Extensive experiments on several benchmarks show that our proposed method is effective and beneficial for OOD detection, outperforming previous state-of-the-art methods.