SwAV

2000년 도입 · 논문 55편에서 사용

SwaV, or Swapping Assignments Between Views, is a self-supervised learning approach that takes advantage of contrastive methods without requiring to compute pairwise comparisons. Specifically, it simultaneously clusters the data while enforcing consistency between cluster assignments produced for different augmentations (or views) of the same image, instead of comparing features directly as in contrastive learning. Simply put, SwaV uses a swapped prediction mechanism where we predict the cluster assignment of a view from the representation of another view.

출처: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments

소개 논문: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments