  We propose a self-supervised Gaussian ATtention network for image Clustering
(GATCluster). Rather than extracting intermediate features first and then
performing the traditional clustering algorithm, GATCluster directly outputs
semantic cluster labels without further post-processing. Theoretically, we give
a Label Feature Theorem to guarantee the learned features are one-hot encoded
vectors, and the trivial solutions are avoided. To train the GATCluster in a
completely unsupervised manner, we design four self-learning tasks with the
constraints of transformation invariance, separability maximization, entropy
analysis, and attention mapping. Specifically, the transformation invariance
and separability maximization tasks learn the relationships between sample
pairs. The entropy analysis task aims to avoid trivial solutions. To capture
the object-oriented semantics, we design a self-supervised attention mechanism
that includes a parameterized attention module and a soft-attention loss. All
the guiding signals for clustering are self-generated during the training
process. Moreover, we develop a two-step learning algorithm that is
memory-efficient for clustering large-size images. Extensive experiments
demonstrate the superiority of our proposed method in comparison with the
state-of-the-art image clustering benchmarks. Our code has been made publicly
available at https://github.com/niuchuangnn/GATCluster.
