Robust Self

Robust self-learning focuses on training machine learning models to be resilient to noisy data, adversarial attacks, and distributional shifts, often leveraging unlabeled data to improve performance and generalization. Current research emphasizes techniques like self-training with improved pseudo-labeling strategies, ensemble methods to enhance confidence measures, and the incorporation of self-supervised learning paradigms, including those utilizing Lie groups to model data transformations. These advancements are crucial for deploying reliable AI systems in diverse real-world applications, particularly in domains like medical image analysis and large-scale conversational AI, where data quality and robustness are paramount.