Exemplar Free Class Incremental Learning

Exemplar-free class incremental learning (EFCIL) addresses the challenge of training machine learning models on a sequence of tasks without storing data from previous tasks, aiming to prevent catastrophic forgetting. Current research focuses on developing methods that leverage techniques like adaptive covariance matrices, analytic learning solutions, and knowledge distillation to maintain performance on old classes while learning new ones, often employing prototype-based or dual-stream architectures. EFCIL is significant because it enables continuous learning in resource-constrained environments and addresses privacy concerns associated with data storage, with potential applications in areas like robotics and personalized medicine.