# A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal

arXiv: https://arxiv.org/abs/2209.13917 · HTML: https://mlsanity.com/paper/2209.13917/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 28 Sep 2022.

## Builds on (most cited first, 16 of 16)

- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Efficient Lifelong Learning with A-GEM](https://mlsanity.com/paper/1812.00420/) (2018)
- [Dark Experience for General Continual Learning: a Strong, Simple Baseline](https://mlsanity.com/paper/2004.07211/) (2020)
- [Large Scale Incremental Learning](https://mlsanity.com/paper/1905.13260/) (2019)
- [Online Continual Learning with Maximally Interfered Retrieval](https://mlsanity.com/paper/1908.04742/) (2019)
- [CORe50: a New Dataset and Benchmark for Continuous Object Recognition](https://mlsanity.com/paper/1705.03550/) (2017)
- [Rainbow Memory: Continual Learning with a Memory of Diverse Samples](https://mlsanity.com/paper/2103.17230/) (2021)
- [Online Continual Learning in Image Classification: An Empirical Survey](https://mlsanity.com/paper/2101.10423/) (2021)
- [Co2L: Contrastive Continual Learning](https://mlsanity.com/paper/2106.14413/) (2021)
- [Linear Mode Connectivity in Multitask and Continual Learning](https://mlsanity.com/paper/2010.04495/) (2020)
- [Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning](https://mlsanity.com/paper/2103.13885/) (2021)
- [Rehearsal revealed: The limits and merits of revisiting samples in continual learning](https://mlsanity.com/paper/2104.07446/) (2021)

## Built on by (most cited first, 30 of 32)

- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [Learning without Forgetting for Vision-Language Models](https://mlsanity.com/paper/2305.19270/) (2023)
- [PILOT: A Pre-Trained Model-Based Continual Learning Toolbox](https://mlsanity.com/paper/2309.07117/) (2023)
- [The Ideal Continual Learner: An Agent That Never Forgets](https://mlsanity.com/paper/2305.00316/) (2023)
- [Online-LoRA: Task-free Online Continual Learning via Low Rank Adaptation](https://mlsanity.com/paper/2411.05663/) (2024)
- [A Comprehensive Empirical Evaluation on Online Continual Learning](https://mlsanity.com/paper/2308.10328/) (2023)
- [Catastrophic Forgetting in Deep Learning: A Comprehensive Taxonomy](https://mlsanity.com/paper/2312.10549/) (2023)
- [Summarizing Stream Data for Memory-Constrained Online Continual Learning](https://mlsanity.com/paper/2305.16645/) (2023)
- [CBA: Improving Online Continual Learning via Continual Bias Adaptor](https://mlsanity.com/paper/2308.06925/) (2023)
- [Orchestrate Latent Expertise: Advancing Online Continual Learning with Multi-Level Supervision and Reverse Self-Distillation](https://mlsanity.com/paper/2404.00417/) (2024)
- [Improving Online Continual Learning Performance and Stability with Temporal Ensembles](https://mlsanity.com/paper/2306.16817/) (2023)
- [On the Effectiveness of LayerNorm Tuning for Continual Learning in Vision Transformers](https://mlsanity.com/paper/2308.09610/) (2023)
- [Non-Exemplar Online Class-incremental Continual Learning via Dual-prototype Self-augment and Refinement](https://mlsanity.com/paper/2303.10891/) (2023)
- [Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning](https://mlsanity.com/paper/2402.09542/) (2024)
- [F-OAL: Forward-only Online Analytic Learning with Fast Training and Low Memory Footprint in Class Incremental Learning](https://mlsanity.com/paper/2403.15751/) (2024)
- [Continual Learning in Open-vocabulary Classification with Complementary Memory Systems](https://mlsanity.com/paper/2307.01430/) (2023)
- [Metalearning Continual Learning Algorithms](https://mlsanity.com/paper/2312.00276/) (2023)
- [Online Continual Learning via Logit Adjusted Softmax](https://mlsanity.com/paper/2311.06460/) (2023)
- [Doubly Perturbed Task Free Continual Learning](https://mlsanity.com/paper/2312.13027/) (2023)
- [SPD-CFL: Stepwise Parameter Dropout for Efficient Continual Federated Learning](https://mlsanity.com/paper/2405.09394/) (2024)
- [Reconstruct before Query: Continual Missing Modality Learning with Decomposed Prompt Collaboration](https://mlsanity.com/paper/2403.11373/) (2024)
- [On the Effectiveness of Equivariant Regularization for Robust Online Continual Learning](https://mlsanity.com/paper/2305.03648/) (2023)
- [A Natural Gas Consumption Forecasting System for Continual Learning Scenarios based on Hoeffding Trees with Change Point Detection Mechanism](https://mlsanity.com/paper/2309.03720/) (2023)
- [HPCR: Holistic Proxy-based Contrastive Replay for Online Continual Learning](https://mlsanity.com/paper/2309.15038/) (2023)
- [Not Just Object, But State: Compositional Incremental Learning without Forgetting](https://mlsanity.com/paper/2411.01739/) (2024)
- [EFC++: Elastic Feature Consolidation with Prototype Re-balancing for Cold Start Exemplar-free Incremental Learning](https://mlsanity.com/paper/2503.10439/) (2025)
- [Information-Theoretic Generalization Bounds of Replay-based Continual Learning](https://mlsanity.com/paper/2507.12043/) (2025)
- [Decision Boundary-aware Knowledge Consolidation Generates Better Instance-Incremental Learner](https://mlsanity.com/paper/2406.03065/) (2024)
- [Rehearsal-free and Task-free Online Continual Learning With Contrastive Prompt](https://mlsanity.com/paper/2510.00467/) (2025)
- [DualMix: Unleashing the Potential of Data Augmentation for Online Class-Incremental Learning](https://mlsanity.com/paper/2303.07864/) (2023)

## Abstract (arXiv)

Online continual learning (OCL) aims to train neural networks incrementally from a non-stationary data stream with a single pass through data. Rehearsal-based methods attempt to approximate the observed input distributions over time with a small memory and revisit them later to avoid forgetting. Despite its strong empirical performance, rehearsal methods still suffer from a poor approximation of the loss landscape of past data with memory samples. This paper revisits the rehearsal dynamics in online settings. We provide theoretical insights on the inherent memory overfitting risk from the viewpoint of biased and dynamic empirical risk minimization, and examine the merits and limits of repeated rehearsal. Inspired by our analysis, a simple and intuitive baseline, Repeated Augmented Rehearsal (RAR), is designed to address the underfitting-overfitting dilemma of online rehearsal. Surprisingly, across four rather different OCL benchmarks, this simple baseline outperforms vanilla rehearsal by 9%-17% and also significantly improves state-of-the-art rehearsal-based methods MIR, ASER, and SCR. We also demonstrate that RAR successfully achieves an accurate approximation of the loss landscape of past data and high-loss ridge aversion in its learning trajectory. Extensive ablation studies are conducted to study the interplay between repeated and augmented rehearsal and reinforcement learning (RL) is applied to dynamically adjust the hyperparameters of RAR to balance the stability-plasticity trade-off online. Code is available at https://github.com/YaqianZhang/RepeatedAugmentedRehearsal