  Recent advances in image clustering typically focus on learning better deep
representations. In contrast, we present an orthogonal approach that does not
rely on abstract features but instead learns to predict image transformations
and performs clustering directly in image space. This learning process
naturally fits in the gradient-based training of K-means and Gaussian mixture
model, without requiring any additional loss or hyper-parameters. It leads us
to two new deep transformation-invariant clustering frameworks, which jointly
learn prototypes and transformations. More specifically, we use deep learning
modules that enable us to resolve invariance to spatial, color and
morphological transformations. Our approach is conceptually simple and comes
with several advantages, including the possibility to easily adapt the desired
invariance to the task and a strong interpretability of both cluster centers
and assignments to clusters. We demonstrate that our novel approach yields
competitive and highly promising results on standard image clustering
benchmarks. Finally, we showcase its robustness and the advantages of its
improved interpretability by visualizing clustering results over real
photograph collections.
