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

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

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

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 …

The downstream accuracy of self-supervised methods is tightly linked to the proxy task
solved during training and the quality of the gradients extracted from it. Richer and more …

The superiority of deeply learned representations relies on large-scale labeled datasets.
However, annotating data are usually expensive or even infeasible in some scenarios. To …

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

The instance discrimination paradigm has become dominant in unsupervised learning. It
always adopts a teacher-student framework, in which the teacher provides embedded …

Mixup is a well-known data-dependent augmentation technique for DNNs, consisting of two
sub-tasks: mixup generation and classification. However, the recent dominant online training …