Hyperspherically Regularized Networks for Self-Supervision
This work addresses an incremental improvement in self-supervised learning for computer vision by enhancing representation quality in BYOL, which could benefit researchers and practitioners in the field.
The paper tackled the problem of poorly distributed image representations in BYOL's self-supervised learning by showing that incorporating feature diversity from contrastive losses and using hyperspherical energy regularization improves representation uniformity and inter-class separability, leading to better performance in downstream tasks.
Bootstrap Your Own Latent (BYOL) introduced an approach to self-supervised learning avoiding the contrastive paradigm and subsequently removing the computational burden of negative sampling associated with such methods. However, we empirically find that the image representations produced under the BYOL's self-distillation paradigm are poorly distributed in representation space compared to contrastive methods. This work empirically demonstrates that feature diversity enforced by contrastive losses is beneficial to image representation uniformity when employed in BYOL, and as such, provides greater inter-class representation separability. Additionally, we explore and advocate the use of regularization methods, specifically the layer-wise minimization of hyperspherical energy (i.e. maximization of entropy) of network weights to encourage representation uniformity. We show that directly optimizing a measure of uniformity alongside the standard loss, or regularizing the networks of the BYOL architecture to minimize the hyperspherical energy of neurons can produce more uniformly distributed and therefore better performing representations for downstream tasks.