Video Summary

The paper studies the problem of training a neural network to perform a task when only a small amount of training data is available. The authors propose a method called self-supervised learning, which trains the network to perform a task by first pre-training it on a related task that has a lot of training data, and then fine-tuning it on the target task. The authors evaluate their method on several tasks, and show that it achieves state-of-the-art results on some of them.