  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 including
linear evaluation and fine-tuning scenarios with multi-label datasets in the
face understanding domain. In almost all tested scenarios, VCL surpasses the
performance of state-of-the-art self-supervised methods, achieving a noteworthy
increase in accuracy.
