Self-supervised learning aims to learn representations from the data itself without explicit manual supervision. Existing efforts ignore a crucial aspect of self-supervised learning - the ability to scale to large amount of data because self-supervision requires no manual labels. In this work, we revisit this principle and scale two popular self-supervised approaches to 100 million images. We show that by scaling on various axes (including data size and problem 'hardness'), one can largely match or even exceed the performance of supervised pre-training on a variety of tasks such as object detection, surface normal estimation (3D) and visual navigation using reinforcement learning. Scaling these methods also provides many interesting insights into the limitations of current self-supervised techniques and evaluations. We conclude that current self-supervised methods are not 'hard' enough to take full advantage of large scale data and do not seem to learn effective high level semantic representations. We also introduce an extensive benchmark across 9 different datasets and tasks. We believe that such a benchmark along with comparable evaluation settings is necessary to make meaningful progress.

本文探讨了自监督学习的可扩展性，并在大规模数据集上评估了两种流行的自监督学习方法，并发现当前自监督学习方法没有充分利用大规模数据，且无法学习到有效的高层语义表示。同时，介绍了9个不同数据集和任务的自监督学习基准测试，必要时开展可比较的评估设置，以实现具有意义的进展。

自监督视觉表示学习的扩展和基准测试