  Can we automatically group images into semantically meaningful clusters when
ground-truth annotations are absent? The task of unsupervised image
classification remains an important, and open challenge in computer vision.
Several recent approaches have tried to tackle this problem in an end-to-end
fashion. In this paper, we deviate from recent works, and advocate a two-step
approach where feature learning and clustering are decoupled. First, a
self-supervised task from representation learning is employed to obtain
semantically meaningful features. Second, we use the obtained features as a
prior in a learnable clustering approach. In doing so, we remove the ability
for cluster learning to depend on low-level features, which is present in
current end-to-end learning approaches. Experimental evaluation shows that we
outperform state-of-the-art methods by large margins, in particular +26.6% on
CIFAR10, +25.0% on CIFAR100-20 and +21.3% on STL10 in terms of classification
accuracy. Furthermore, our method is the first to perform well on a large-scale
dataset for image classification. In particular, we obtain promising results on
ImageNet, and outperform several semi-supervised learning methods in the
low-data regime without the use of any ground-truth annotations. The code is
made publicly available at
https://github.com/wvangansbeke/Unsupervised-Classification.
