Self-supervised learning (SSL) has recently achieved impressive performance on various
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …

In deep learning research, self-supervised learning (SSL) has received great attention,
triggering interest within both the computer vision and remote sensing communities. While …

In this article, we propose a unified framework to solve the following two challenging
problems in incomplete multi-view representation learning: i) how to learn a consistent …

Recently, contrastive learning (CL) is a promising way of learning discriminative
representations from time series data. In the representation hierarchy, semantic information …

Self-supervised learning (SSL) is rapidly closing the gap with supervised methods on large
computer vision benchmarks. A successful approach to SSL is to learn embeddings which …

Learning medical visual representations directly from paired radiology reports has become
an emerging topic in representation learning. However, existing medical image-text joint …

Unsupervised semantic segmentation aims to discover and localize semantically meaningful
categories within image corpora without any form of annotation. To solve this task …

We propose a simple but effective source-free domain adaptation (SFDA) method. Treating
SFDA as an unsupervised clustering problem and following the intuition that local neighbors …

In this paper, we study two challenging problems in incomplete multi-view clustering
analysis, namely, i) how to learn an informative and consistent representation among …

Multi-view clustering can explore common semantics from multiple views and has attracted
increasing attention. However, existing works punish multiple objectives in the same feature …