Exploring Simple Siamese Representation Learning

What makes contrastive learning work so well? This paper highlights the contribution of the Siamese architecture as a compliment to data augmentation and shows how Siamese nets a stop-gradient operation in the negative encoder is all you need for strong contrastive self-supervised learning results. The paper also presents an interesting k-Means style explanation of the optimization problem contrastive self-supervised learning solves. Thanks for watching! Please Subscribe! Paper Links: SimSiam: https://ar