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 …

Deep learning has achieved great success in learning features from massive remote
sensing images (RSIs). To better understand the connection between three feature learning …

Although augmentations (eg, perturbation of graph edges, image crops) boost the efficiency
of Contrastive Learning (CL), feature level augmentation is another plausible …

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 …

Contrastive Learning (CL) has achieved impressive performance in self-supervised learning
tasks, showing superior generalization ability. Inspired by the success, adopting CL into …

In recent decades, wildfires have caused tremendous property losses, fatalities, and
extensive damage to forest ecosystems. Inspired by the abundance of publicly available …

Abstract Language-supervised vision models have recently attracted great attention in
computer vision. A common approach to build such models is to use contrastive learning on …

The surprising discovery of the BYOL method shows the negative samples can be replaced
by adding the prediction head to the network. It is mysterious why even when there exist …

Oversmoothing is a common phenomenon in a wide range of Graph Neural Networks
(GNNs) and Transformers, where performance worsens as the number of layers increases …

Medical data often exhibits long-tail distributions with heavy class imbalance, which
naturally leads to difficulty in classifying the minority classes (ie, boundary regions or rare …