Learning Transformation

Learning transformation focuses on developing data representations that improve the performance of machine learning models, particularly in handling noisy data, preserving privacy, and enhancing generalization across diverse datasets. Current research emphasizes learning optimal transformations through self-supervised learning, leveraging techniques like neural networks and information-theoretic approaches to design data-driven transformations for tasks such as anomaly detection, data augmentation, and model quantization. These advancements are significant for improving model robustness, privacy, and efficiency across various applications, including autonomous driving, drug discovery, and natural language processing.