Recent advances in self-supervised learning with instance-level contrastive objectives
facilitate unsupervised clustering. However, a standalone datum is not perceiving the …

In this paper, we propose an online clustering method called Contrastive Clustering (CC)
which explicitly performs the instance-and cluster-level contrastive learning. To be specific …

Self-supervised learning (SSL) learns high-quality representations from large pools of
unlabeled training data. As datasets grow larger, it becomes crucial to identify the examples …

Recent developments of the application of Contrastive Learning in Semi-Supervised
Learning (SSL) have demonstrated significant advancements, as a result of its exceptional …

Recently, contrastive learning has risen to be a promising approach for large-scale self-
supervised learning. However, theoretical understanding of how it works is still unclear. In …

Most approaches for self-supervised learning (SSL) are optimised on curated balanced
datasets, eg ImageNet, despite the fact that natural data usually exhibits long-tail …

As an indispensable branch of unsupervised learning, deep clustering is rapidly emerging
along with the growth of deep neural networks. Recently, contrastive learning paradigm has …

Recently, self-supervised learning has attracted great attention, since it only requires
unlabeled data for model training. Contrastive learning is one popular method for self …

Self-Supervised Learning (SSL) has enhanced the learning process of semantic
representations from images. SSL has reduced the need for annotating or labelling the data …

Understanding self-supervised learning is important but challenging. Previous theoretical
works study the role of pretraining losses, and view neural networks as general black boxes …