CLR, a novel contrastive learning method using graph-based sample relationships. This approach outperformed traditional ...

Secondly, we introduce a contrastive learning framework, using the original graph as the anchor, to further explore the differences and similarities between the anchor graph and the tensorized ...

Let’s start with an introduction to Graph Contrastive Learning and know why it doesn’t need any human annotations. Graph Contrastive Learning (GCL), as the name implies, contrasts graph samples and ...

On this basis, we propose an Augmentation-free Graph Contrastive Transformer optimized through nearest neighbors to avoid model degradation; 2) Different similarity measures are designed for positive ...

SCoAMPS integrates pseudo-labeling techniques with contrastive learning by generating contrastive views through multiple encoders, selecting positive and negative samples using pseudo-label similarity ...

2.3 GCATCMDA Figure 1 illustrates the workflow of GCATCMDA, a model based on graph neural networks and contrastive learning for predicting effective candidate sets of microbe-disease associations.

Now, the similarity between two augmented versions of an image is calculated using cosine similarity. SimCLR uses “NT-Xent loss” (Normalised Temperature-Scaled Cross-Entropy Loss), which is known as ...