Topic
disentanglement
Artificial Intelligence #artificial intelligence#graph neural networks
Boundary Embedding Shaping with Adaptive Contrastive Learning Boosts GNN Classification by 3.3%
Graph neural networks suffer from structural entanglement, especially near class boundaries. A new plug-in module called Boundary Embedding Shaping (BES) uses adaptive contrastive learning to selectively suppress spurious correlations, boosting GCN node classification by an average of 3.3% (up to 5% on WikiCS) and improving link prediction accuracy.
Jun 20, 2026 1 source
Artificial Intelligence #audio deepfake detection#disentanglement
Dual-Granularity Orthogonal Disentanglement: New Framework Boosts Generalizable Audio Deepfake Detection
A new paper on arXiv proposes a dual-granularity orthogonal disentanglement framework for generalizable audio deepfake detection. The method enforces sample-level cosine orthogonality and batch-level cross-covariance regularization to avoid speaker identity leakage. Experiments show equal error rates of 1.35%, 7.88%, and 21.58% on standard benchmarks.
Jun 16, 2026 1 source