Progressive Learning

Progressive learning is a machine learning paradigm focusing on incrementally increasing model capacity or complexity during training, aiming for efficient and robust learning. Current research emphasizes automated methods for scheduling this growth, often incorporating techniques like momentum growth and curriculum learning across diverse model architectures, including Vision Transformers and large language models. This approach offers significant advantages in resource efficiency, particularly for large models, and improves performance in various applications such as image processing, natural language processing, and robotics, by mitigating issues like catastrophic forgetting and enabling effective transfer learning across domains.