Noisy Label

Noisy label learning (NLL) tackles the challenge of training machine learning models on datasets containing inaccurate labels, a common problem in large-scale data collection. Current research focuses on developing robust algorithms and model architectures, such as vision transformers and graph neural networks, that can effectively mitigate the negative impact of noisy labels, often employing techniques like sample selection, loss function modification, and self-supervised learning. These advancements are crucial for improving the reliability and generalizability of machine learning models across various applications, from image classification and natural language processing to medical image analysis and remote sensing. The ultimate goal is to build more robust and reliable AI systems that can handle the imperfections inherent in real-world data.