Online Continual Learning

Online continual learning (OCL) focuses on training machine learning models that can adapt to continuously arriving, non-stationary data streams without catastrophic forgetting of previously learned information. Current research emphasizes efficient algorithms that address issues like model throughput limitations, biased forgetting towards newer tasks, and imbalanced data distributions, often employing techniques like experience replay, contrastive learning, and adaptive bias correction within various neural network architectures, including transformers and ResNets. OCL's significance lies in its potential to create more robust and adaptable AI systems for real-world applications such as autonomous driving, fault diagnosis, and personalized recommendation systems, where continuous learning from evolving data is crucial.