SLA converts hard-counting of high-loss samples into a continuous noisiness score by standardizing fold-level validation losses and aggregating them over multiple cross-validation runs, showing better performance than baselines on fundus data.
Fine samples for learning with noisy labels
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Task-Agnostic Noisy Label Detection via Standardized Loss Aggregation
SLA converts hard-counting of high-loss samples into a continuous noisiness score by standardizing fold-level validation losses and aggregating them over multiple cross-validation runs, showing better performance than baselines on fundus data.