Unsupervised Continual Learning

Unsupervised continual learning (UCL) focuses on enabling artificial intelligence systems to learn from a continuous stream of unlabeled data without forgetting previously acquired knowledge. Current research emphasizes developing algorithms that mitigate catastrophic forgetting, often employing contrastive learning, knowledge distillation, and generative replay techniques within various architectures, including variational autoencoders and spiking neural networks. This field is crucial for building more robust and adaptable AI systems capable of operating in dynamic, real-world environments where labeled data is scarce or unavailable, impacting applications ranging from robotics to personalized medicine.