arXiv Machine Learning
Jul 17
Interleaved Noise Injection Improves Clean, Corrupted, and OOD Performance
arXiv:2607. 14466v1 Announce Type: new Abstract: Noise injection is a well-known technique in stochastic optimization.
SAGG: Sample-Adaptive Gradient Gating for Robust Multimodal Learning under Heterogeneous Corruption
SAGG: Sample-Adaptive Gradient Gating for Robust Multimodal Learning under Heterogeneous Corruption proposes a new method for handling sample-heterogeneous corruption in multimodal training. The authors prove that batch-level, sample-agnostic linear estimators with a shared modulation parameter inevitably incur bias, and that a sample-level all-or-nothing gating strategy is the only unbiased approach within a natural estimator class. SAGG implements a binary retain-or-discard decision per sample using an online feature-norm quality test and a truncation mechanism for variance control, and demonstrates convergence to clean-loss stationary points while achieving superior performance over ten existing methods on Kinetics-Sounds and UCF-101 under various corruption scenarios.
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arXiv Machine Learning
Oct 7
Improving Mixup Calibration with Wasserstein Distributionally Robust Optimization
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arXiv AI
Aug 25
Mitigating Sample-Level Imbalance via Probabilistic Separation for Adaptive Multimodal Fusion
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arXiv AI
Sep 16
Dual Randomized Smoothing: Beyond Global Noise Variance
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arXiv AI
Jun 16
ALCL: An Adaptive Log-Correntropy Loss for Robust Learning under Non-Gaussian Noise
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arXiv Machine Learning
Oct 23
Can Entry-Wise Clipping Give Spectral Control of Stochastic Gradients?
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arXiv Machine Learning
Sep 22
Whitening Inverts the Hierarchy: What the Norm of a Whitened Embedding Measures
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arXiv Computer Vision
Oct 26
Feature-Space Smoothing: Certified Robustness of Deep Representations
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arXiv Computer Vision
Sep 15
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arXiv Computer Vision
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arXiv Machine Learning
Jun 24
Robust and Fast Training via Per-Sample Clipping
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