Free Learning

Free learning, encompassing data-free and model-free approaches, aims to train machine learning models without relying on large labeled datasets or explicit model specifications. Current research focuses on developing techniques like diffusion models, deep neural networks, and contrastive learning to generate synthetic data or learn directly from system interactions, often employing reinforcement learning or policy gradient methods. This field is significant for its potential to reduce the high cost and effort associated with data acquisition and annotation, enabling efficient model training in resource-constrained scenarios and facilitating applications in areas like healthcare, robotics, and autonomous systems.