Semi Supervised Continual Learning

Semi-supervised continual learning (SSCL) aims to develop machine learning models that can incrementally learn from a stream of data where only a portion of the data is labeled, addressing the limitations of both supervised continual learning (requiring fully labeled data) and traditional semi-supervised learning (lacking the sequential nature of real-world data). Current research focuses on efficient resource allocation strategies, particularly for computational budgets and limited labeled data, often employing contrastive learning, self-training, and knowledge distillation techniques within various model architectures to mitigate catastrophic forgetting and improve knowledge transfer across tasks. SSCL's significance lies in its potential to create more robust and adaptable AI systems capable of learning continuously from real-world data streams, where fully labeled datasets are often unavailable or impractical to obtain.