Hard Negative Mixing for Contrastive Learning

Understand

Contrastive learning has become a key component of self-supervised learning approaches for computer vision.

• By learning to embed two augmented versions of the same image close to each other and to push the embeddings of different images apart, one can train highly transferable visual representations.
• As revealed by recent studies, heavy data augmentation and large sets of negatives are both crucial in learning such representations.
• At the same time, data mixing strategies either at the image or the feature level improve both supervised and semi-supervised learning by synthesizing novel examples, forcing networks to learn more robust features.