Clustering is a fundamental machine learning task, which aim at assigning instances into
groups so that similar samples belong to the same cluster while dissimilar samples belong …

Generalized category discovery (GCD) is a recently proposed open-world problem, which
aims to automatically cluster partially labeled data. The main challenge is that the unlabeled …

Clustering has been a major research topic in the field of machine learning, one to which
Deep Learning has recently been applied with significant success. However, an aspect of …

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

Self-supervised representation learning for visual pre-training has achieved remarkable
success with sample (instance or pixel) discrimination and semantics discovery of instance …

Abstract Graph Contrastive Learning (GCL) has recently drawn much research interest for
learning generalizable node representations in a self-supervised manner. In general, the …

Achieving artificially intelligent-native wireless networks is necessary for the operation of
future 6G applications such as the metaverse. Nonetheless, current communication …

The pre-trained language models, eg, ELMo and BERT, have recently achieved promising
performance improvement in a wide range of NLP tasks, because they can output strong …

In this article, a new unsupervised contrastive clustering (CC) model is introduced, namely,
image CC with self-learning pairwise constraints (ICC-SPC). This model is designed to …

Recently, contrastive learning has gained increasing attention as a research topic for image-
clustering tasks. However, most contrastive learning-based clustering models focus only on …