Video Summary

The paper proposes a new method for training neural networks that is based on the idea of self-supervised learning. The method, called self-supervised contrastive learning (SLC), involves training the network to predict the relative positions of its inputs. This has the effect of forcing the network to learn features that are invariant to small changes in the input, which makes it more robust to noise and outliers. The authors evaluated SLC on a variety of tasks, including image classification, object detection, and natural language processing, and showed that it achieved state-of-the-art results on all of them.