Learning Multi Phase Incremental Task

Learning multi-phase incremental tasks focuses on enabling artificial intelligence systems to continuously learn new tasks without forgetting previously acquired knowledge, a challenge known as catastrophic forgetting. Current research emphasizes methods like low-rank weight updates, winning subnetwork identification, and meta-learning approaches that synthesize training data to improve generalization to new tasks. These advancements are crucial for developing more robust and adaptable AI systems applicable to diverse fields, including natural language processing, robotics, and computer vision, where continuous learning from streaming data is essential.