LiNC learns per-sample trust parameters during standard training, separates clean, ambiguous, and noisy labels with a 3-component GMM, and corrects only the noisy ones, improving last-epoch accuracy under symmetric label noise.
Training deep neural-networks using a noise adap- tation layer
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LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling
LiNC learns per-sample trust parameters during standard training, separates clean, ambiguous, and noisy labels with a 3-component GMM, and corrects only the noisy ones, improving last-epoch accuracy under symmetric label noise.