FB-NLL decouples user clustering from training dynamics by using subspace similarity on feature covariances and corrects noisy labels via directional alignment in learned feature space.
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Random pixel permutation destroys local correlations in images, causing standard CNN classification accuracy to drop depending on class similarities while dilated convolutions recover some performance.
citing papers explorer
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FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning
FB-NLL decouples user clustering from training dynamics by using subspace similarity on feature covariances and corrects noisy labels via directional alignment in learned feature space.
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Convolutional Neural Networks on Randomized Data
Random pixel permutation destroys local correlations in images, causing standard CNN classification accuracy to drop depending on class similarities while dilated convolutions recover some performance.