Can Generative Models Improve Self-Supervised Representation Learning?
Arash Afkanpour Vahid Reza Khazaie Sana Ayromlou Fereshteh Forghani
The rapid advancement in self-supervised learning (SSL) has highlighted its
potential to leverage unlabeled data for learning powerful visual
representations. However, existing SSL approaches, particularly those employing
different views of the same image, often rely on a limited set of predefined
data augmentations. This constrains the diversity and quality of
transformations, 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 experimental results demonstrate that our
framework significantly enhances the quality of learned visual representations.
This research demonstrates that incorporating generative models into the SSL
workflow opens new avenues for exploring the potential of unlabeled visual
data. This development paves the way for more robust and versatile
representation learning techniques.