Continual Learning Scenario

Continual learning aims to enable machine learning models to acquire new knowledge incrementally without forgetting previously learned information, a crucial challenge for real-world applications with evolving data streams. Current research focuses on mitigating "catastrophic forgetting" through techniques like parameter-efficient fine-tuning, knowledge distillation, and memory replay, often applied to vision-language models, diffusion models, and neural networks of varying sizes. These advancements are significant for building more adaptable and robust AI systems across diverse domains, from audio analysis and robotics to natural gas consumption forecasting, where data distributions change over time.