Abstract
Concept drift, temporal dependence, and catastrophic forgetting represent major challenges when learning from data streams. While Streaming Machine Learning and Continual Learning (CL) address these issues separately, recent efforts in Streaming Continual Learning (SCL) aim to unify them. In this work, we introduce MAGIC Net, a novel SCL approach that integrates CL-inspired architectural strategies with recurrent neural networks to tame temporal dependence. MAGIC Net continuously learns, looks back at past knowledge by applying learnable masks over frozen weights, and expands its architecture when necessary. It performs all operations online, ensuring inference availability at all times. Experiments on synthetic and real-world streams show that it improves adaptation to new concepts, limits memory usage, and mitigates forgetting.
Abstract (translated)
概念漂移、时间依赖性和灾难性遗忘是处理数据流学习时面临的重大挑战。尽管流式机器学习和连续学习（CL）分别解决了这些问题，但最近的Streaming Continual Learning（SCL）努力旨在将它们统一起来。在这项工作中，我们介绍了MAGIC Net，这是一种新颖的SCL方法，它结合了受CL启发的架构策略与循环神经网络来处理时间依赖性问题。MAGIC Net持续学习，并通过在冻结权重上应用可学习的掩码来回溯过去的知识，在必要时扩展其架构。该模型的所有操作都在线进行，确保始终可以进行推断。实验结果表明，无论是在合成数据流还是现实世界的数据流中，MAGIC Net都能改善对新概念的适应性、限制内存使用，并减轻遗忘现象。
URL
https://arxiv.org/abs/2603.08600
