Self Supervised Continual Learning

Self-supervised continual learning (SSCL) aims to build AI systems that efficiently learn from unlabeled data streams without catastrophic forgetting of previously acquired knowledge. Current research focuses on developing methods that balance stability (preserving past knowledge) and plasticity (adapting to new data), often employing techniques like contrastive learning, memory-enhanced models, and progressive layer freezing within various architectures. This field is significant because it addresses the limitations of traditional self-supervised learning in dynamic environments, paving the way for more robust and adaptable AI systems across diverse applications like speech recognition, human activity recognition, and medical image analysis.