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

Graph contrastive learning aims to achieve an effective measurement of the similarity and dissimilarity between graphs G 1 and G 2 by learning a mapping function f ⋅. For a given pair of graphs G 1 = ...

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 ...

To tackle this challenge, we propose an extension to a contrastive learning approach utilizing graphs to construct a meaningful embedding space. Our approach demonstrates the continuous mapping of ...

First microbe-microbe and disease-disease Gaussian kernel similarity networks are constructed using known associations. The model then integrates graph neural networks and contrastive learning ...