Don't Drift Away: Advances and Applications of Streaming and Continual Learning

Non-stationary environments subject to concept drift require the design of adaptive models that can continuously learn and update. Two primary research communities have emerged to address this challenge: Continual Learning (CL) and Streaming Machine Learning (SML). CL manages virtual drifts by learning new concepts without forgetting past knowledge, while SML focuses on real drifts, rapidly adapting to evolving data distributions. However, a unified approach is needed to balance adaptation and knowledge retention. Streaming Continual Learning (SCL) bridges the gap between CL and SML, ensuring models retain useful past information while efficiently adapting to new data. We explore key challenges in SCL, including handling temporal dependencies in data streams and adapting latent representations for personalization and knowledge editing. Additionally, we identify promising SCL benchmarks which can foster and promote a unified research effort between CL and SML.