  A good clustering algorithm can discover natural groupings in data. These
groupings, if used wisely, provide a form of weak supervision for learning
representations. In this work, we present Clustering-based Contrastive Learning
(CCL), a new clustering-based representation learning approach that uses labels
obtained from clustering along with video constraints to learn discriminative
face features. We demonstrate our method on the challenging task of learning
representations for video face clustering. Through several ablation studies, we
analyze the impact of creating pair-wise positive and negative labels from
different sources. Experiments on three challenging video face clustering
datasets: BBT-0101, BF-0502, and ACCIO show that CCL achieves a new
state-of-the-art on all datasets.
