  This paper proposes to perform online clustering by conducting twin
contrastive learning (TCL) at the instance and cluster level. Specifically, we
find that when the data is projected into a feature space with a dimensionality
of the target cluster number, the rows and columns of its feature matrix
correspond to the instance and cluster representation, respectively. Based on
the observation, for a given dataset, the proposed TCL first constructs
positive and negative pairs through data augmentations. Thereafter, in the row
and column space of the feature matrix, instance- and cluster-level contrastive
learning are respectively conducted by pulling together positive pairs while
pushing apart the negatives. To alleviate the influence of intrinsic
false-negative pairs and rectify cluster assignments, we adopt a
confidence-based criterion to select pseudo-labels for boosting both the
instance- and cluster-level contrastive learning. As a result, the clustering
performance is further improved. Besides the elegant idea of twin contrastive
learning, another advantage of TCL is that it could independently predict the
cluster assignment for each instance, thus effortlessly fitting online
scenarios. Extensive experiments on six widely-used image and text benchmarks
demonstrate the effectiveness of TCL. The code will be released on GitHub.
