  The rapid advancement in self-supervised learning (SSL) has highlighted its
potential to leverage unlabeled data for learning rich visual representations.
However, the existing SSL techniques, particularly those employing different
augmentations of the same image, often rely on a limited set of simple
transformations that are not representative of real-world data variations. This
constrains the diversity and quality of samples, which leads to sub-optimal
representations. In this paper, we introduce a novel framework that enriches
the SSL paradigm by utilizing generative models to produce semantically
consistent image augmentations. By directly conditioning generative models on a
source image representation, our method enables the generation of diverse
augmentations while maintaining the semantics of the source image, thus
offering a richer set of data for self-supervised learning. Our extensive
experimental results on various SSL methods demonstrate that our framework
significantly enhances the quality of learned visual representations by up to
10\% Top-1 accuracy in downstream tasks. This research demonstrates that
incorporating generative models into the SSL workflow opens new avenues for
exploring the potential of synthetic data. This development paves the way for
more robust and versatile representation learning techniques.
