Rehearsal Free Class Incremental Learning
Rehearsal-free class-incremental learning (CIL) aims to train machine learning models that can continuously learn new classes without access to previously seen data, addressing the "catastrophic forgetting" problem. Current research focuses on leveraging techniques like learnable prompts, parameter-efficient subnetworks, and knowledge distillation to maintain performance across increasingly numerous classes. These methods show promise in improving the efficiency and scalability of continual learning, particularly relevant for applications with limited memory or privacy constraints and dynamic data streams. The development of robust and efficient rehearsal-free CIL algorithms is crucial for advancing artificial intelligence in real-world scenarios where continuous adaptation is essential.