Abstract
By simultaneously learning visual features and data grouping, deep clustering has shown impressive ability to deal with unsupervised learning for structure analysis of high-dimensional visual data. Existing deep clustering methods typically rely on local learning constraints based on inter-sample relations and/or self-estimated pseudo labels. This is susceptible to the inevitable errors distributed in the neighbourhoods and suffers from error-propagation during training. In this work, we propose to solve this problem by learning the most confident clustering solution from all the possible separations, based on the observation that assigning samples from the same semantic categories into different clusters will reduce both the intra-cluster compactness and inter-cluster diversity, i.e. lower partition confidence. Specifically, we introduce a novel deep clustering method named PartItion Confidence mAximisation (PICA). It is established on the idea of learning the most semantically plausible data separation, in which all clusters can be mapped to the ground-truth classes one-to-one, by maximising the "global" partition confidence of clustering solution. This is realised by introducing a differentiable partition uncertainty index and its stochastic approximation as well as a principled objective loss function that minimises such index, all of which together enables a direct adoption of the conventional deep networks and mini-batch based model training. Extensive experiments on six widely-adopted clustering benchmarks demonstrate our model's performance superiority over a wide range of the state-of-the-art approaches. The code is available online.