Deep Clustering by Semantic Contrastive Learning
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Whilst contrastive learning has achieved remarkable success in self-supervised representation learning, its potential for deep clustering remains unknown. This is due to its fundamental limitation that the instance discrimination strategy it takes is not class sensitive and hence unable to reason about the underlying decision boundaries between semantic concepts or classes. In this work, we solve this problem by introducing a novel variant called Semantic Contrastive Learning (SCL). It explores the characteristics of both conventional contrastive learning and deep clustering by imposing distance-based cluster structures on unlabelled training data and also introducing a discriminative contrastive loss formulation. For explicitly modelling class boundaries on-the-fly, we further formulate a clustering consistency condition on the two different predictions given by visual similarities and semantic decision boundaries. By advancing implicit representation learning towards explicit understandings of visual semantics, SCL can amplify jointly the strengths of contrastive learning and deep clustering in a unified approach. Extensive experiments show that the proposed model outperforms the state-of-the-art deep clustering methods on six challenging object recognition benchmarks, especially on finer-grained and larger datasets.
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