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

Self-supervised visual representation learning aims to learn useful representations without
relying on human annotations. Joint embedding approach bases on maximizing the …

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

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

Unsupervised representation learning with contrastive learning achieves great success
recently. However, these methods have to duplicate each training batch to construct …

Self-supervised learning aims to learn representations from the data itself without explicit
manual supervision. Existing efforts ignore a crucial aspect of self-supervised learning-the …

We present Transformation Invariance and Covariance Contrast (TiCo) for self-supervised
visual representation learning. Similar to other recent self-supervised learning methods, our …

Siamese networks have become a common structure in various recent models for
unsupervised visual representation learning. These models maximize the similarity between …

This paper is concerned with self-supervised learning for small models. The problem is
motivated by our empirical studies that while the widely used contrastive self-supervised …