BMWReg: Brownian-diffusive, Multiview, Whitening Regulararizations for Self-supervised Learning

Image credit: Unsplash

Abstract

Recent self-supervised representation learning methods depend on joint embedding learning with siamese-like networks, trained by maximizing the agreement of differently augmented same-class representations (positive pairs). Using positive pairs may avoid dealing with computationally demanding negatives, but so-called mode collapse may occur without any implicit biases in the learning architecture. In this paper, we propose a new loss function, called BMWReg, which induces an implicit contrastive effect in the embedding space, effectively preventing a mode collapse. BMWReg consists of the following three regularization terms. (i) a Brownian diffusive loss, which induces a Brownian motion in the embedding space so that embeddings are uniformly distributed on the unit hypersphere. (ii) A multi-view centroid loss, which applies an attractive force to pull together multiple augmented representations of the same image into the geometric centroid. (iii) A whitening loss, which decorrelates the different feature dimensions in the latent space. We evaluate BMWReg on two visual benchmarks – ImageNet-100 and STL-10. In addition, we also show that applying our regularization term to other methods further improves their performance and stabilize the training by preventing a mode collapse.

Publication
In International Conference on Machine Learning Workshop
Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.
Create your slides in Markdown - click the Slides button to check out the example.

Supplementary notes can be added here, including code, math, and images.