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Deepfake detectors can be made far more robust to real-world image corruptions by training on heavily degraded data and ensembling complementary feature streams.
Naive nearest neighbor retrieval cripples tabular in-context learning for clinical risk prediction, but task-aligned retrieval unlocks significant gains, especially when data is messy.
