# Online Continual Learning on Sequences

arXiv: https://arxiv.org/abs/2003.09114 · HTML: https://mlsanity.com/paper/2003.09114/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2020.

## Built on by (most cited first, 14 of 14)

- [Online Continual Learning in Image Classification: An Empirical Survey](https://mlsanity.com/paper/2101.10423/) (2021)
- [Avalanche: an End-to-End Library for Continual Learning](https://mlsanity.com/paper/2104.00405/) (2021)
- [Continual Learning for Real-World Autonomous Systems: Algorithms, Challenges and Frameworks](https://mlsanity.com/paper/2105.12374/) (2021)
- [A Sparse Quantized Hopfield Network for Online-Continual Memory](https://mlsanity.com/paper/2307.15040/) (2023)
- [Sustainable Artificial Intelligence through Continual Learning](https://mlsanity.com/paper/2111.09437/) (2021)
- [Online Continual Graph Learning](https://mlsanity.com/paper/2508.03283/) (2025)
- [Robust Continual Learning through a Comprehensively Progressive Bayesian Neural Network](https://mlsanity.com/paper/2202.13369/) (2022)
- [Architect, Regularize and Replay (ARR): a Flexible Hybrid Approach for Continual Learning](https://mlsanity.com/paper/2301.02464/) (2023)
- [Dynamic Nested Hierarchies: Pioneering Self-Evolution in Machine Learning Architectures for Lifelong Intelligence](https://mlsanity.com/paper/2511.14823/) (2025)
- [Memory-Efficient Continual Learning Object Segmentation for Long Video](https://mlsanity.com/paper/2309.15274/) (2023)
- [Generative System Dynamics in Recurrent Neural Networks](https://mlsanity.com/paper/2504.13951/) (2025)
- [CRoSS: A Continual Robotic Simulation Suite for Scalable Reinforcement Learning with High Task Diversity and Realistic Physics Simulation](https://mlsanity.com/paper/2602.04868/) (2026)
- [Don't Look Back in Anger: MAGIC Net for Streaming Continual Learning with Temporal Dependence](https://mlsanity.com/paper/2603.08600/) (2026)
- [Preventing Latent Rehearsal Decay in Online Continual SSL with SOLAR](https://mlsanity.com/paper/2604.10586/) (2026)

## Abstract (arXiv)

Online continual learning (OCL) refers to the ability of a system to learn over time from a continuous stream of data without having to revisit previously encountered training samples. Learning continually in a single data pass is crucial for agents and robots operating in changing environments and required to acquire, fine-tune, and transfer increasingly complex representations from non-i.i.d. input distributions. Machine learning models that address OCL must alleviate textitcatastrophic forgetting in which hidden representations are disrupted or completely overwritten when learning from streams of novel input. In this chapter, we summarize and discuss recent deep learning models that address OCL on sequential input through the use (and combination) of synaptic regularization, structural plasticity, and experience replay. Different implementations of replay have been proposed that alleviate catastrophic forgetting in connectionists architectures via the re-occurrence of (latent representations of) input sequences and that functionally resemble mechanisms of hippocampal replay in the mammalian brain. Empirical evidence shows that architectures endowed with experience replay typically outperform architectures without in (online) incremental learning tasks.