  While state-of-the-art contrastive Self-Supervised Learning (SSL) models
produce results competitive with their supervised counterparts, they lack the
ability to infer latent variables. In contrast, prescribed latent variable (LV)
models enable attributing uncertainty, inducing task specific compression, and
in general allow for more interpretable representations. In this work, we
introduce LV approximations to large scale contrastive SSL models. We
demonstrate that this addition improves downstream performance (resulting in
96.42% and 77.49% test top-1 fine-tuned performance on CIFAR10 and ImageNet
respectively with a ResNet50) as well as producing highly compressed
representations (588x reduction) that are useful for interpretability,
classification and regression downstream tasks.

Machine Learning