Contrastive Multiview Coding

2000년 도입 · 논문 5편에서 사용

Contrastive Multiview Coding (CMC) is a self-supervised learning approach, based on CPC, that learns representations that capture information shared between multiple sensory views. The core idea is to set an anchor view and the sample positive and negative data points from the other view and maximise agreement between positive pairs in learning from two views. Contrastive learning is used to build the embedding.

출처: Contrastive Multiview Coding

소개 논문: Contrastive Multiview Coding