MoCo v2

2000년 도입 · 논문 30편에서 사용

MoCo v2 is an improved version of the Momentum Contrast self-supervised learning algorithm. Motivated by the findings presented in the SimCLR paper, authors: - Replace the 1-layer fully connected layer with a 2-layer MLP head with ReLU for the unsupervised training stage. - Include blur augmentation. - Use cosine learning rate schedule. These modifications enable MoCo to outperform the state-of-the-art SimCLR with a smaller batch size and fewer epochs.

출처: Improved Baselines with Momentum Contrastive Learning

소개 논문: Improved Baselines with Momentum Contrastive Learning