Whilst contrastive learning has recently brought notable benefits to deep
clustering of unlabelled images by learning sample-specific discriminative
visual features, its potential for explicitly inferring class decision
boundaries is less well understood. This is because its instance discrimination
strategy is not class sensitive, therefore, the clusters derived on the
resulting sample-specific feature space are not optimised for corresponding to
meaningful class decision boundaries. In this work, we solve this problem by
introducing Semantic Contrastive Learning (SCL). SCL imposes explicitly
distance-based cluster structures on unlabelled training data by formulating a
semantic (cluster-aware) contrastive learning objective. Moreover, we introduce
a clustering consistency condition to be satisfied jointly by both instance
visual similarities and cluster decision boundaries, and concurrently
optimising both to reason about the hypotheses of semantic ground-truth classes
(unknown/unlabelled) on-the-fly by their consensus. This semantic contrastive
learning approach to discovering unknown class decision boundaries has
considerable advantages to unsupervised learning of object recognition tasks.
Extensive experiments show that SCL outperforms state-of-the-art contrastive
learning and deep clustering methods on six object recognition benchmarks,
especially on the more challenging finer-grained and larger datasets.

本文提出了一种语义对比学习的方法（SCL），将距离的聚类结构引入到无标注数据的特征空间中，并通过优化实例视觉相似性和聚类决策边界的一致性来推理出语义上的实体类别，以在发现未知类别决策边界方面具有显着优势。实验证明，SCL 在六个物体识别基准测试中优于最先进的对比学习和深度聚类方法，尤其是在更具挑战性的精细和较大的数据集上。