Bearing fault diagnosis is of great importance to ensure the safe and stable operation of
mechanical equipment. The actual collected bearing fault signals are susceptible to strong …

Early diagnosis of lung cancer is critical as it can save people's lives. Long-range
dependencies within volumetric medical images are essential attributes for accurate lung …

Learning with self-derived targets provides a non-contrastive method for unsupervised
image representation learning, where the variety in targets is crucial. Recent work has …

Contrastive learning has achieved great success in many applications. A key step in
contrastive learning is to find a positive sample and negative samples. Traditional methods …

Graph contrastive learning (GCL) is an effective paradigm for node representation learning
in graphs. The key components hidden behind GCL are data augmentation and positive …

Fake news detection on social media is crucial to purifying the online environment and
protecting public safety. Many existing methods explore the news propagation structures …

In recent years, a variety of contrastive learning based unsupervised visual representation
learning methods have been designed and achieved great success in many visual tasks …

Graph contrastive learning (GCL) is receiving growing attention due to its promising self-
supervised learning capability on graph-structured data. While GCL approaches have …

The widespread proliferation of social media has brought convenience to individuals, which
has also intensified the propagation of rumors. Existing graph-based approaches primarily …