  Clustering is one of the fundamental tasks in computer vision and pattern
recognition. Recently, deep clustering methods (algorithms based on deep
learning) have attracted wide attention with their impressive performance. Most
of these algorithms combine deep unsupervised representation learning and
standard clustering together. However, the separation of representation
learning and clustering will lead to suboptimal solutions because the two-stage
strategy prevents representation learning from adapting to subsequent tasks
(e.g., clustering according to specific cues). To overcome this issue, efforts
have been made in the dynamic adaption of representation and cluster
assignment, whereas current state-of-the-art methods suffer from heuristically
constructed objectives with representation and cluster assignment alternatively
optimized. To further standardize the clustering problem, we audaciously
formulate the objective of clustering as finding a precise feature as the cue
for cluster assignment. Based on this, we propose a general-purpose deep
clustering framework which radically integrates representation learning and
clustering into a single pipeline for the first time. The proposed framework
exploits the powerful ability of recently developed generative models for
learning intrinsic features, and imposes an entropy minimization on the
distribution of the cluster assignment by a dedicated variational algorithm.
Experimental results show that the performance of the proposed method is
superior, or at least comparable to, the state-of-the-art methods on the
handwritten digit recognition, fashion recognition, face recognition and object
recognition benchmark datasets.
