Label Noise Robustness

Label noise robustness in machine learning focuses on developing models that are resilient to inaccuracies in training data labels. Current research explores various strategies, including post-training correction methods like those using singular value decomposition, and modifications to training algorithms such as sharpness-aware minimization or contrastive learning within deep neural networks. These efforts aim to improve model generalization and performance in real-world scenarios where perfectly labeled data is often unavailable, impacting fields like medical image analysis and other applications relying on large-scale datasets.