CANOLA estimates label noise and performs cautious iterative soft-label refinement to correct corrupted training data, reporting 19-52% error reduction versus prior methods on six datasets.
KNN- enhanced deep learning against noisy labels,
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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A challenge submission system using expanded training data, feature-specific branches, and post-processing achieves up to 81.25% hierarchical F1 on BSD10k-v1.2.
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Noise-Aware Framework for Correcting Corrupted Labels
CANOLA estimates label noise and performs cautious iterative soft-label refinement to correct corrupted training data, reporting 19-52% error reduction versus prior methods on six datasets.
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A Multi-Branch Hierarchy-Aware Framework for Heterogeneous Audio Classification
A challenge submission system using expanded training data, feature-specific branches, and post-processing achieves up to 81.25% hierarchical F1 on BSD10k-v1.2.