Recently, self-supervised learning (SSL) has achieved tremendous success in learning image representation. Despite the empirical success, most self-supervised learning methods are rather “inefficient” learners, typically taking hundreds of training epochs to fully converge. In this work, we
show that the key towards efficient self-supervised learning is to increase the number of crops from
each image instance. Leveraging one of the state-of-the-art SSL method, we introduce a simplistic form of self-supervised learning method called Extreme-Multi-Patch Self-Supervised-Learning
(EMP-SSL) that does not rely on many heuristic techniques for SSL such as weight sharing between
the branches, feature-wise normalization, output quantization, and stop gradient, etc, and reduces
the training epochs by two orders of magnitude. We show that the proposed method is able to
converge to 85.1% on CIFAR-10, 58.5% on CIFAR-100, 38.1% on Tiny ImageNet and 58.5% on
ImageNet-100 in just one epoch. Furthermore, the proposed method achieves 91.5% on CIFAR-10,
70.1% on CIFAR-100, 51.5% on Tiny ImageNet and 78.9% on ImageNet-100 with linear probing
in less than ten training epochs. In addition, we show that EMP-SSL shows significantly better
transferability to out-of-domain datasets compared to baseline SSL methods. We will release the
code in https://github.com/tsb0601/EMP-SSL.

https://arxiv.org/pdf/2304.03977.pdf