# An Investigation of Replay-based Approaches for Continual Learning

arXiv: https://arxiv.org/abs/2108.06758 · HTML: https://mlsanity.com/paper/2108.06758/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 49 references that are in the map.
- First posted on arXiv 15 Aug 2021.

## Builds on (most cited first, 30 of 36)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [Experience Replay for Continual Learning](https://mlsanity.com/paper/1811.11682/) (2018)
- [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)
- [PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning](https://mlsanity.com/paper/1711.05769/) (2017)
- [Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence](https://mlsanity.com/paper/1801.10112/) (2018)
- [Overcoming catastrophic forgetting with hard attention to the task](https://mlsanity.com/paper/1801.01423/) (2018)
- [Gradient based sample selection for online continual learning](https://mlsanity.com/paper/1903.08671/) (2019)
- [Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference](https://mlsanity.com/paper/1810.11910/) (2018)
- [Online Continual Learning with Maximally Interfered Retrieval](https://mlsanity.com/paper/1908.04742/) (2019)
- [Expert Gate: Lifelong Learning with a Network of Experts](https://mlsanity.com/paper/1611.06194/) (2016)
- [PathNet: Evolution Channels Gradient Descent in Super Neural Networks](https://mlsanity.com/paper/1701.08734/) (2017)
- [Measuring Catastrophic Forgetting in Neural Networks](https://mlsanity.com/paper/1708.02072/) (2017)
- [Learning without Memorizing](https://mlsanity.com/paper/1811.08051/) (2018)
- [Overcoming Catastrophic Forgetting by Incremental Moment Matching](https://mlsanity.com/paper/1703.08475/) (2017)
- [Selective Experience Replay for Lifelong Learning](https://mlsanity.com/paper/1802.10269/) (2018)
- [Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines](https://mlsanity.com/paper/1810.12488/) (2018)
- [Mnemonics Training: Multi-Class Incremental Learning without Forgetting](https://mlsanity.com/paper/2002.10211/) (2020)
- [Using Hindsight to Anchor Past Knowledge in Continual Learning](https://mlsanity.com/paper/2002.08165/) (2020)
- [Generative replay with feedback connections as a general strategy for continual learning](https://mlsanity.com/paper/1809.10635/) (2018)
- [Optimal Continual Learning has Perfect Memory and is NP-hard](https://mlsanity.com/paper/2006.05188/) (2020)

## Built on by (most cited first, 26 of 26)

- [Continual Learning with Foundation Models: An Empirical Study of Latent Replay](https://mlsanity.com/paper/2205.00329/) (2022)
- [Exploring Example Influence in Continual Learning](https://mlsanity.com/paper/2209.12241/) (2022)
- [Online-LoRA: Task-free Online Continual Learning via Low Rank Adaptation](https://mlsanity.com/paper/2411.05663/) (2024)
- [Enhancing Continual Learning in Visual Question Answering with Modality-Aware Feature Distillation](https://mlsanity.com/paper/2406.19297/) (2024)
- [Dataset Condensation with Latent Quantile Matching](https://mlsanity.com/paper/2406.09860/) (2024)
- [Replay Consolidation with Label Propagation for Continual Object Detection](https://mlsanity.com/paper/2409.05650/) (2024)
- [Overcoming Catastrophic Forgetting in Tabular Data Classification: A Pseudorehearsal-based approach](https://mlsanity.com/paper/2407.09039/) (2024)
- [A Study of Continual Learning Methods for Q-Learning](https://mlsanity.com/paper/2206.03934/) (2022)
- [Adiabatic replay for continual learning](https://mlsanity.com/paper/2303.13157/) (2023)
- [Integrating Curricula with Replays: Its Effects on Continual Learning](https://mlsanity.com/paper/2307.05747/) (2023)
- [Domain Adaptation for Time series Transformers using One-step fine-tuning](https://mlsanity.com/paper/2401.06524/) (2024)
- [SPARC: Subspace-Aware Prompt Adaptation for Robust Continual Learning in LLMs](https://mlsanity.com/paper/2502.02909/) (2025)
- [SLE-FNO: Single-Layer Extensions for Task-Agnostic Continual Learning in Fourier Neural Operators](https://mlsanity.com/paper/2603.20410/) (2026)
- [Keep Moving: identifying task-relevant subspaces to maximise plasticity for newly learned tasks](https://mlsanity.com/paper/2310.04741/) (2023)
- [Informed Spectral Normalized Gaussian Processes for Trajectory Prediction](https://mlsanity.com/paper/2403.11966/) (2024)
- [H2C: Hippocampal Circuit-inspired Continual Learning for Lifelong Trajectory Prediction in Autonomous Driving](https://mlsanity.com/paper/2508.01158/) (2025)
- [Unforgettable Lessons from Forgettable Images: Intra-Class Memorability Matters in Computer Vision](https://mlsanity.com/paper/2412.20761/) (2024)
- [Tackling Distribution Shift in LLM via KILO: Knowledge-Instructed Learning for Continual Adaptation](https://mlsanity.com/paper/2508.03571/) (2025)
- [Expanding continual few-shot learning benchmarks to include recognition of specific instances](https://mlsanity.com/paper/2209.07863/) (2022)
- [Technical Report for the 5th CLVision Challenge at CVPR: Addressing the Class-Incremental with Repetition using Unlabeled Data -- 4th Place Solution](https://mlsanity.com/paper/2503.15697/) (2025)
- [KDC-Diff: A Latent-Aware Diffusion Model with Knowledge Retention for Memory-Efficient Image Generation](https://mlsanity.com/paper/2505.06995/) (2025)
- [Continual Multiple Instance Learning for Hematologic Disease Diagnosis](https://mlsanity.com/paper/2508.04368/) (2025)
- [Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach](https://mlsanity.com/paper/2508.09510/) (2025)
- [CLeAN: Continual Learning Adaptive Normalization in Dynamic Environments](https://mlsanity.com/paper/2603.17548/) (2026)
- [An Empirical Analysis of Continual Learning for Heterogeneous Medical Visual Question Answering](https://mlsanity.com/paper/2607.12048/) (2026)
- [Attention-based Experience Replay Framework for Continual Learning of Agnostic Time Series Forecasting Models](https://mlsanity.com/paper/2607.20493/) (2026)

## Abstract (arXiv)

Continual learning (CL) is a major challenge of machine learning (ML) and describes the ability to learn several tasks sequentially without catastrophic forgetting (CF). Recent works indicate that CL is a complex topic, even more so when real-world scenarios with multiple constraints are involved. Several solution classes have been proposed, of which so-called replay-based approaches seem very promising due to their simplicity and robustness. Such approaches store a subset of past samples in a dedicated memory for later processing: while this does not solve all problems, good results have been obtained. In this article, we empirically investigate replay-based approaches of continual learning and assess their potential for applications. Selected recent approaches as well as own proposals are compared on a common set of benchmarks, with a particular focus on assessing the performance of different sample selection strategies. We find that the impact of sample selection increases when a smaller number of samples is stored. Nevertheless, performance varies strongly between different replay approaches. Surprisingly, we find that the most naive rehearsal-based approaches that we propose here can outperform recent state-of-the-art methods.