Whitening for Self-Supervised Representation Learning

Most of the self-supervised representation learning methods are based on the contrastive loss and the instance-discrimination task, where augmented versions of the same image instance ("positives") are contrasted with instances extracted from other images ("negatives"). For the learning to be effective, a lot of negatives should be compared with a positive pair, which is computationally demanding. In this paper, we propose a different direction and a new loss function for selfsupervised representation learning which is based on the whitening of the latentspace features. The whitening operation has a "scattering" effect on the batch samples, which compensates the use of negatives, avoiding degenerate solutions where all the sample representations collapse to a single point. Our Whitening MSE (W-MSE) loss does not require special heuristics (e.g. Since negatives are not needed, we can extract multiple positive pairs from the same image instance. We empirically show that W-MSE is competitive with respect to popular, more complex self-supervised methods.