Contrastive Learning is Just Meta-Learning

Contrastive learning has recently taken off as a paradigm for learning from unlabeled data. In this paper, we discuss the close relationship between contrastive learning and meta-learning, and in fact show that contrastive learning can be interpreted as a special case of meta-learning with a certain task distribution. We complement this observation by showing that established meta-learning methods, such as Prototypical Networks, achieve comparable performance to SimCLR when paired with this task distribution. This close relationship can be leveraged by taking established techniques from the meta-learning literature, such as task-based data augmentation, and showing that they benefit contrastive learning as well. These tricks also benefit state-of-the-art self-supervised learners without using negative pairs such as BYOL, which achieves $94.6\%$ accuracy on CIFAR-10 using a self-supervised ResNet-18 feature extractor trained with our meta-learning tricks.

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