Introduces Relative Geometric Conflict (RGC) using gradient direction comparison and empirical Fisher trace factorization to improve reliability estimation beyond loss or confidence signals in noisy-label training.
Psscl: A progressive sample selection framework with contrastive loss designed for noisy labels.Pattern Recognition, 161:111284, 2025
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Radial-Angular Geometry for Reliable Update Diagnosis in Noisy-Label Learning
Introduces Relative Geometric Conflict (RGC) using gradient direction comparison and empirical Fisher trace factorization to improve reliability estimation beyond loss or confidence signals in noisy-label training.