Barlow Twins: Self-Supervised Learning via Redundancy Reduction

Barlow Twins is a self-supervised learning approach that leverages redundancy reduction to create embeddings invariant to input distortions.

It uses two identical neural networks to minimize redundancy between vector components, ensuring robust representations.

This method is competitive with state-of-the-art self-supervised learning techniques.