Authors: Amal Shaheena, Nairouz Mrabahb, Riadh Ksantinia, Abdulla Alqaddoumia
Abstract: The recent advances in deep clustering have been made possible by significant
progress in self-supervised and pseudo-supervised learning. However, the
trade-off between self-supervision and pseudo-supervision can give rise to
three primary issues. The joint training causes Feature Randomness and Feature
Drift, whereas the independent training causes Feature Randomness and Feature
Twist. In essence, using pseudo-labels generates random and unreliable
features. The combination of pseudo-supervision and self-supervision drifts the
reliable clustering-oriented features. Moreover, moving from self-supervision
to pseudo-supervision can twist the curved latent manifolds. This paper
addresses the limitations of existing deep clustering paradigms concerning
Feature Randomness, Feature Drift, and Feature Twist. We propose a new paradigm
with a new strategy that replaces pseudo-supervision with a second round of
self-supervision training. The new strategy makes the transition between
instance-level self-supervision and neighborhood-level self-supervision
smoother and less abrupt. Moreover, it prevents the drifting effect that is
caused by the strong competition between instance-level self-supervision and
clustering-level pseudo-supervision. Moreover, the absence of the
pseudo-supervision prevents the risk of generating random features. With this
novel approach, our paper introduces a Rethinking of the Deep Clustering
Paradigms, denoted by R-DC. Our model is specifically designed to address three
primary challenges encountered in Deep Clustering: Feature Randomness, Feature
Drift, and Feature Twist. Experimental results conducted on six datasets have
shown that the two-level self-supervision training yields substantial
improvements.
Source: http://arxiv.org/abs/2503.03733v1
