Towards Efficient and Effective Deep Clustering with Dynamic Grouping
  and Prototype Aggregation
Haixin Zhang Dong Huang
Previous contrastive deep clustering methods mostly focus on instance-level
information while overlooking the member relationship within groups/clusters,
which may significantly undermine their representation learning and clustering
capability. Recently, some group-contrastive methods have been developed,
which, however, typically rely on the samples of the entire dataset to obtain
pseudo labels and lack the ability to efficiently update the group assignments
in a batch-wise manner. To tackle these critical issues, we present a novel
end-to-end deep clustering framework with dynamic grouping and prototype
aggregation, termed as DigPro. Specifically, the proposed dynamic grouping
extends contrastive learning from instance-level to group-level, which is
effective and efficient for timely updating groups. Meanwhile, we perform
contrastive learning on prototypes in a spherical feature space, termed as
prototype aggregation, which aims to maximize the inter-cluster distance.
Notably, with an expectation-maximization framework, DigPro simultaneously
takes advantage of compact intra-cluster connections, well-separated clusters,
and efficient group updating during the self-supervised training. Extensive
experiments on six image benchmarks demonstrate the superior performance of our
approach over the state-of-the-art. Code is available at
https://github.com/Regan-Zhang/DigPro.