Continual Learning Performance

Continual learning aims to enable artificial intelligence models to acquire new knowledge incrementally without forgetting previously learned information, a challenge known as catastrophic forgetting. Current research focuses on mitigating this issue through various techniques, including architectural modifications (e.g., using hypernetworks, pairwise layers, or bio-inspired designs), regularization methods (e.g., spectral regularization, functional regularization), and leveraging pre-trained models (e.g., prompt-based methods and CLIP). These advancements are crucial for building more robust and adaptable AI systems capable of learning continuously from ever-changing data streams, with implications for applications ranging from robotics to personalized medicine.