#BYOL

Explore a practical implementation of BYOL in Pytorch for self-supervised learning that simplifies the process by removing the need for contrastive learning and negative pairs. Seamlessly integrate with any image-based neural network using unlabelled data. Features include recent updates like group norm and weight standardization for optimization. Delve into augmentation and distributed training to improve network efficiency on supervised tasks, providing a cost-effective solution.