  We present a technique to improve the transferability of deep representations
learned on small labeled datasets by introducing self-supervised tasks as
auxiliary loss functions. While recent approaches for self-supervised learning
have shown the benefits of training on large unlabeled datasets, we find
improvements in generalization even on small datasets and when combined with
strong supervision. Learning representations with self-supervised losses
reduces the relative error rate of a state-of-the-art meta-learner by 5-25% on
several few-shot learning benchmarks, as well as off-the-shelf deep networks on
standard classification tasks when training from scratch. We find the benefits
of self-supervision increase with the difficulty of the task. Our approach
utilizes the images within the dataset to construct self-supervised losses and
hence is an effective way of learning transferable representations without
relying on any external training data.
