Fast Adaptation

Fast adaptation in machine learning focuses on enabling models to quickly and efficiently adjust to new tasks or data distributions with minimal retraining. Current research emphasizes techniques like meta-learning (including Model-Agnostic Meta-Learning and its variants), test-time training, and parameter-efficient fine-tuning methods such as Low-Rank Adaptation (LoRA), often applied to pre-trained foundation models. This area is crucial for deploying AI in dynamic real-world scenarios, improving efficiency in applications ranging from robotics and recommendation systems to medical image analysis and disaster response.