Published in NeurIPS, 2022
This article proposes a self-supervised learning method that uses a second-order statistics-based mutual information measure that reﬂects the level of correlation among its arguments and prevents dimensional collapse by encouraging the spread of information across the whole feature space.
Recommended citation: Ozsoy, S., Hamdan, S.S., Arik, S.Ö., Yuret, D., & Erdogan, A.T. (2022). Self-Supervised Learning with an Information Maximization Criterion. ArXiv, abs/2209.07999. https://arxiv.org/abs/2209.07999