  Unsupervised out-of-distribution (OOD) Detection aims to separate the samples
falling outside the distribution of training data without label information.
Among numerous branches, contrastive learning has shown its excellent
capability of learning discriminative representation in OOD detection. However,
for its limited vision, merely focusing on instance-level relationship between
augmented samples, it lacks attention to the relationship between samples with
same semantics. Based on the classic contrastive learning, we propose
Cluster-aware Contrastive Learning (CCL) framework for unsupervised OOD
detection, which considers both instance-level and semantic-level information.
Specifically, we study a cooperation strategy of clustering and contrastive
learning to effectively extract the latent semantics and design a cluster-aware
contrastive loss function to enhance OOD discriminative ability. The loss
function can simultaneously pay attention to the global and local relationships
by treating both the cluster centers and the samples belonging to the same
cluster as positive samples. We conducted sufficient experiments to verify the
effectiveness of our framework and the model achieves significant improvement
on various image benchmarks.
