  Deep clustering outperforms conventional clustering by mutually promoting
representation learning and cluster assignment. However, most existing deep
clustering methods suffer from two major drawbacks. First, most cluster
assignment methods are based on simple distance comparison and highly dependent
on the target distribution generated by a handcrafted nonlinear mapping. These
facts largely limit the possible performance that deep clustering methods can
reach. Second, the clustering results can be easily guided towards wrong
direction by the misassigned samples in each cluster. The existing deep
clustering methods are incapable of discriminating such samples. To address
these issues, a novel modular Self-Evolutionary Clustering (Self-EvoC)
framework is constructed, which boosts the clustering performance by
classification in a self-supervised manner. Fuzzy theory is used to score the
sample membership with probability which evaluates the intermediate clustering
result certainty of each sample. Based on which, the most reliable samples can
be selected and augmented. The augmented data are employed to fine-tune an
off-the-shelf deep network classifier with the labels from the clustering,
which results in a model to generate the target distribution. The proposed
framework can efficiently discriminate sample outliers and generate better
target distribution with the assistance of self-supervised classifier.
Extensive experiments indicate that the Self-EvoC remarkably outperforms
state-of-the-art deep clustering methods on three benchmark datasets.
