arXiv Machine Learning
Jun 5
Towards Label-Noise Resistant Learning via Optimal Brain Damage Masking
arXiv:2508. 09697v3 Announce Type: replace Abstract: Noisy labels are inevitable in real-world scenarios.
Label-Noise Resistant Learning via Optimal Brain Damage Masking
arXiv:2508. 09697v4 Announce Type: replace Abstract: Noisy labels are inevitable in real-world multimedia applications.
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Dropout and Random Gradient Masking Are Asymptotically Equivalent in Large ResNets
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FedLAS: Feature-Modulated Bidirectional Label Smoothing for Neural Network Calibration
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arXiv Machine Learning
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XConv: Low-memory stochastic backpropagation for convolutional layers
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An Adaptive Data cleaning Framework for Noisy Label Detection
arXiv:2606. 07086v1 Announce Type: cross Abstract: Deep neural networks (DNNs) excel in computer vision tasks given large annotated datasets.
arXiv Machine Learning
Jul 21
Robust Losses from Univariate Base Functions for Noisy-Label Learning
arXiv:2607. 16768v1 Announce Type: new Abstract: Learning with noisy labels is a fundamental problem in training reliable deep neural networks.
arXiv Machine Learning
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Simplicity Suffices for Parameter Noise Injection in Stochastic Gradient Descent
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arXiv Machine Learning
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