Can Generative Models Improve Self-Supervised Representation Learning?

Understand

The rapid advancement in self-supervised representation learning has highlighted its potential to leverage unlabeled data for learning rich visual representations.

• However, the existing techniques, particularly those employing different augmentations of the same image, often rely on a limited set of simple transformations that cannot fully capture variations in the real world.
• This constrains the diversity and quality of samples, which leads to sub-optimal representations.
• In this paper, we introduce a framework that enriches the self-supervised learning (SSL) paradigm by utilizing generative models to produce semantically consistent image augmentations.