  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.
