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

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

The recently advanced unsupervised learning approaches use the siamese-like framework
to compare two" views" from the same image for learning representations. Making the two …

By leveraging contrastive learning, clustering, and other pretext tasks, unsupervised
methods for learning image representations have reached impressive results on standard …

Cluster discrimination is an effective pretext task for unsupervised representation learning,
which often consists of two phases: clustering and discrimination. Clustering is to assign …

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

Unsupervised visual representation learning has gained much attention from the computer
vision community because of the recent achievement of contrastive learning. Most of the …

Joint clustering and feature learning methods have shown remarkable performance in
unsupervised representation learning. However, the training schedule alternating between …

Unsupervised visual representation learning remains a largely unsolved problem in
computer vision research. Among a big body of recently proposed approaches for …

Recent single image unsupervised representation learning techniques show remarkable
success on a variety of tasks. The basic principle in these works is instance discrimination …