6 articles tagged with #label-noise. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers enhance Meta-Weight-Net (MW-Net), a neural network for sample reweighting under distribution shifts, by applying neural architecture search to optimize its structure. The improved approach better handles combined label noise and class imbalance problems that degrade standard MW-Net performance, demonstrating effectiveness on CIFAR-10 and CIFAR-100 datasets.
Researchers introduce RIVET, a training framework that uses idempotency constraints to improve voice attribute editing models' robustness to noisy or inconsistent labels in large-scale speech datasets. By enforcing the property that repeated applications produce identical results, the method acts as an implicit regularizer that reduces sensitivity to mislabeled training data while preserving speaker identity.
Researchers propose an active learning framework that combines foundation model priors with smaller models to address class imbalance and label noise in real-world datasets. The method achieves over 50% annotation savings compared to existing active learning baselines while maintaining model performance across image and text domains.
Researchers propose NCSAM, a novel optimization-based approach to learning from noisy labels that theoretically connects label noise to Sharpness-Aware Minimization's behavior. The method uses noise-compensated perturbations to reduce memorization of corrupted annotations while maintaining optimization simplicity, demonstrating competitive performance against existing noisy-label learning methods.
Researchers introduce UFO, a framework addressing robust continual graph learning by simultaneously tackling catastrophic forgetting and noisy data supervision in evolving graphs. The method uses flow-based generative modeling to mitigate forgetting and instance-level reliability scoring to handle corrupted labels, demonstrating superior performance across benchmark datasets.
Researchers introduce VisPrompt, a framework that improves prompt learning for vision-language models by injecting visual semantic information to enhance robustness against label noise. The approach keeps pre-trained models frozen while adding minimal trainable parameters, demonstrating superior performance across seven benchmark datasets under both synthetic and real-world noisy conditions.
