This paper presents SimCLR: a simple framework for contrastive learning of visual
representations. We simplify recently proposed contrastive self-supervised learning …

Contrastive learning applied to self-supervised representation learning has seen a
resurgence in recent years, leading to state of the art performance in the unsupervised …

Contrastive learning models based on Siamese structure have demonstrated remarkable
performance in self-supervised learning. Such a success of contrastive learning relies on …

Self-supervised representation learning has made significant leaps fueled by progress in
contrastive learning, which seeks to learn transformations that embed positive input pairs …

Contrastive learning has shown remarkable results in recent self-supervised approaches for
visual representation. By learning to contrast positive pairs' representation from the …

Semi-supervised learning acts as an effective way to leverage massive unlabeled data. In
this paper, we propose a novel training strategy, termed as Semi-supervised Contrastive …

Contrastive learning approaches have achieved great success in learning visual
representations with few labels of the target classes. That implies a tantalizing possibility of …

Contrastive learning methods for unsupervised visual representation learning have reached
remarkable levels of transfer performance. We argue that the power of contrastive learning …

Self-supervised learning (especially contrastive learning) has attracted great interest due to
its huge potential in learning discriminative representations in an unsupervised manner …

Unsupervised image representations have significantly reduced the gap with supervised
pretraining, notably with the recent achievements of contrastive learning methods. These …