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End-to-end meta-learning of neural field representations reveals that first-order approximations discard crucial learning pathways, leading to improved few-shot adaptation and semantic prediction.
The results show that a frozen renal feature extractor can support both detection and representation-level characterization of glomerular abnormalities without using abnormal examples to fit the detector.
