Holistic Reliability Propagation: Decoupling Annotation and Prediction for Robust Noisy-Label

• URL: http://arxiv.org/abs/2605.20725v1
• Date: Wed, 20 May 2026 05:24:37 GMT
• Title: Holistic Reliability Propagation: Decoupling Annotation and Prediction for Robust Noisy-Label
• Abstract summary: Bilevel meta-learning produces two batch-normalized scalars per sample, alpha for the given label and beta for the pseudo-label, without constraining them to sum to one.<n>We use reliability-aware Mixup with global gating on the input branch and beta-gated pseudo-label positives on the contrastive branch.<n>On synthetic and real-world benchmarks, HRP improves average accuracy over strong baselines and remains competitive at the highest noise rates.
• Score: 3.7874902461360627
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Learning with noisy labels in multimedia classification often combines external annotations and model predictions into a single reliability weight, even though the two sources can fail for different reasons. We instead estimate disentangled reliabilities: bilevel meta-learning produces two batch-normalized scalars per sample, alpha for the given label and beta for the pseudo-label, without constraining them to sum to one. Holistic Reliability Propagation (HRP) then routes them to different objectives, using reliability-aware Mixup with global gating on the input branch and beta-gated pseudo-label positives on the contrastive branch. On synthetic and real-world benchmarks, HRP improves average accuracy over strong baselines and remains competitive at the highest noise rates.

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