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Found 3 papers, 2 papers with code
Training Class-Imbalanced Diffusion Model Via Overlap Optimization
To address the observed appearance overlap between synthesized images of rare classes and tail classes, we propose a method based on contrastive learning to minimize the overlap between distributions of synthetic images for different classes.
Understanding Community Bias Amplification in Graph Representation Learning
In this work, we discover a phenomenon of community bias amplification in graph representation learning, which refers to the exacerbation of performance bias between different classes by graph representation learning.
Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition
This work provides a novel theoretical perspective for addressing the problem of imbalanced node classification in GNNs.
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