Self-supervised Variational Contrastive Learning with Applications to Face Understanding

Learning a discriminative semantic space using unlabelled and noisy data remains unaddressed in a multi-label setting. We present a contrastive self-supervised learning method which is robust to data noise, grounded in the domain of variational methods. The method (VCL) utilizes variational contrastive learning with beta-divergence to learn robustly from unlabelled datasets, including uncurated and noisy datasets. We demonstrate the effectiveness of the proposed method through rigorous experiments with multi-label datasets in the face understanding domain, including one where the system is pretrained with web collected face images. Experiments include linear evaluation and fine-tuning scenarios, in addition to verification and face attribute learning tests, showing that the model learns effective embedding representations. In almost all tested scenarios, VCL surpasses the performance of state-of-the-art self-supervised methods.