UCoL: Unsupervised Learning of Discriminative Facial Representations via Uncertainty-Aware Contrast

Abstract

This paper presents Uncertainty-aware Contrastive Learning (UCoL): a fully unsupervised framework for discriminative facial representation learning. Our UCoL is built upon a momentum contrastive network, referred to as Dual-path Momentum Network. Specifically, two flows of pairwise contrastive training are conducted simultaneously: one is formed with intra-instance self augmentation, and the other is to identify positive pairs collected by online pairwise prediction. We introduce a novel uncertainty-aware consistency Knearest neighbors algorithm to generate predicted positive pairs, which enables efficient discriminative learning from large-scale open-world unlabeled data. Experiments show that UCoL significantly improves the baselines of unsupervised models and performs on par with the semi-supervised and supervised face representation learning methods.