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German Research Center for Artificial Intelligence (DFKI), RPTU Kaiserslautern-Landau
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Continual learning for anomaly detection can now effectively handle heterogeneous data without succumbing to catastrophic forgetting or performance drops.
GC-MoE achieves unprecedented accuracy in predicting single-cell gene expression from histology images by effectively modeling cell-type-specific interactions.
Image classification accuracy jumps by nearly 3% simply by whispering text embeddings into the ear of a vision model during training.
Brain tumor segmentation gets a boost in both accuracy and interpretability with SegGuidedNet, a lightweight architecture that rivals ensembles without needing post-hoc explainability.
