# A Comprehensive Empirical Evaluation on Online Continual Learning

arXiv: https://arxiv.org/abs/2308.10328 · HTML: https://mlsanity.com/paper/2308.10328/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 47 references that are in the map.
- First posted on arXiv 20 Aug 2023.

## Builds on (most cited first, 29 of 29)

- [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)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (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)
- [Gradient based sample selection for online continual learning](https://mlsanity.com/paper/1903.08671/) (2019)
- [Class-incremental learning: survey and performance evaluation on image classification](https://mlsanity.com/paper/2010.15277/) (2020)
- [Online Continual Learning with Maximally Interfered Retrieval](https://mlsanity.com/paper/1908.04742/) (2019)
- [Online Continual Learning in Image Classification: An Empirical Survey](https://mlsanity.com/paper/2101.10423/) (2021)
- [New Insights on Reducing Abrupt Representation Change in Online Continual Learning](https://mlsanity.com/paper/2104.05025/) (2021)
- [Self-Supervised Models are Continual Learners](https://mlsanity.com/paper/2112.04215/) (2021)
- [Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning](https://mlsanity.com/paper/2103.13885/) (2021)
- [Representational Continuity for Unsupervised Continual Learning](https://mlsanity.com/paper/2110.06976/) (2021)
- [Continual Pre-Training Mitigates Forgetting in Language and Vision](https://mlsanity.com/paper/2205.09357/) (2022)
- [Probing Representation Forgetting in Supervised and Unsupervised Continual Learning](https://mlsanity.com/paper/2203.13381/) (2022)
- [Online Continual Learning with Natural Distribution Shifts: An Empirical Study with Visual Data](https://mlsanity.com/paper/2108.09020/) (2021)
- [Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference](https://mlsanity.com/paper/2110.10031/) (2021)
- [Continual evaluation for lifelong learning: Identifying the stability gap](https://mlsanity.com/paper/2205.13452/) (2022)
- [PCR: Proxy-based Contrastive Replay for Online Class-Incremental Continual Learning](https://mlsanity.com/paper/2304.04408/) (2023)
- [Continuum: Simple Management of Complex Continual Learning Scenarios](https://mlsanity.com/paper/2102.06253/) (2021)
- [Avalanche: A PyTorch Library for Deep Continual Learning](https://mlsanity.com/paper/2302.01766/) (2023)
- [A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal](https://mlsanity.com/paper/2209.13917/) (2022)
- [Online Continual Learning with Contrastive Vision Transformer](https://mlsanity.com/paper/2207.13516/) (2022)
- [Sequoia: A Software Framework to Unify Continual Learning Research](https://mlsanity.com/paper/2108.01005/) (2021)
- [On the importance of cross-task features for class-incremental learning](https://mlsanity.com/paper/2106.11930/) (2021)
- [Improving Online Continual Learning Performance and Stability with Temporal Ensembles](https://mlsanity.com/paper/2306.16817/) (2023)
- [One Pass ImageNet](https://mlsanity.com/paper/2111.01956/) (2021)
- [SequeL: A Continual Learning Library in PyTorch and JAX](https://mlsanity.com/paper/2304.10857/) (2023)

## Built on by (most cited first, 24 of 24)

- [Elastic Feature Consolidation for Cold Start Exemplar-Free Incremental Learning](https://mlsanity.com/paper/2402.03917/) (2024)
- [The Future of Continual Learning in the Era of Foundation Models: Three Key Directions](https://mlsanity.com/paper/2506.03320/) (2025)
- [Orchestrate Latent Expertise: Advancing Online Continual Learning with Multi-Level Supervision and Reverse Self-Distillation](https://mlsanity.com/paper/2404.00417/) (2024)
- [Parameter-Efficient Continual Fine-Tuning: A Survey](https://mlsanity.com/paper/2504.13822/) (2025)
- [Online Curvature-Aware Replay: Leveraging mathbf2nd Order Information for Online Continual Learning](https://mlsanity.com/paper/2502.01866/) (2025)
- [Federated Continual Learning Goes Online: Uncertainty-Aware Memory Management for Vision Tasks and Beyond](https://mlsanity.com/paper/2405.18925/) (2024)
- [Sequence Transferability and Task Order Selection in Continual Learning](https://mlsanity.com/paper/2502.06544/) (2025)
- [Implicit biases in multitask and continual learning from a backward error analysis perspective](https://mlsanity.com/paper/2311.00235/) (2023)
- [How to Leverage Predictive Uncertainty Estimates for Reducing Catastrophic Forgetting in Online Continual Learning](https://mlsanity.com/paper/2407.07668/) (2024)
- [Online Continual Graph Learning](https://mlsanity.com/paper/2508.03283/) (2025)
- [Fairness Evolution in Continual Learning for Medical Imaging](https://mlsanity.com/paper/2406.02480/) (2024)
- [The impact of model size on catastrophic forgetting in Online Continual Learning](https://mlsanity.com/paper/2407.00176/) (2024)
- [EFC++: Elastic Feature Consolidation with Prototype Re-balancing for Cold Start Exemplar-free Incremental Learning](https://mlsanity.com/paper/2503.10439/) (2025)
- [A Practical Guide to Streaming Continual Learning](https://mlsanity.com/paper/2603.01677/) (2026)
- [CLA: Latent Alignment for Online Continual Self-Supervised Learning](https://mlsanity.com/paper/2507.10434/) (2025)
- [The Unreasonable Effectiveness of Randomized Representations in Online Continual Graph Learning](https://mlsanity.com/paper/2510.06819/) (2025)
- [Sample Weight Estimation Using Meta-Updates for Online Continual Learning](https://mlsanity.com/paper/2401.15973/) (2024)
- [PVBF: A Framework for Mitigating Parameter Variation Imbalance in Online Continual Learning](https://mlsanity.com/paper/2502.17794/) (2025)
- [Multi-level Collaborative Distillation Meets Global Workspace Model: A Unified Framework for OCIL](https://mlsanity.com/paper/2508.08677/) (2025)
- [Online Continual Learning for Time Series: a Natural Score-driven Approach](https://mlsanity.com/paper/2601.12931/) (2026)
- [Routing without Forgetting](https://mlsanity.com/paper/2603.09576/) (2026)
- [Natural Gradient Descent for Online Continual Learning](https://mlsanity.com/paper/2603.20898/) (2026)
- [Preventing Latent Rehearsal Decay in Online Continual SSL with SOLAR](https://mlsanity.com/paper/2604.10586/) (2026)
- [Lost or Hidden? A Concept-Level Forgetting in Supervised Continual Learning](https://mlsanity.com/paper/2605.16374/) (2026)

## Abstract (arXiv)

Online continual learning aims to get closer to a live learning experience by learning directly on a stream of data with temporally shifting distribution and by storing a minimum amount of data from that stream. In this empirical evaluation, we evaluate various methods from the literature that tackle online continual learning. More specifically, we focus on the class-incremental setting in the context of image classification, where the learner must learn new classes incrementally from a stream of data. We compare these methods on the Split-CIFAR100 and Split-TinyImagenet benchmarks, and measure their average accuracy, forgetting, stability, and quality of the representations, to evaluate various aspects of the algorithm at the end but also during the whole training period. We find that most methods suffer from stability and underfitting issues. However, the learned representations are comparable to i.i.d. training under the same computational budget. No clear winner emerges from the results and basic experience replay, when properly tuned and implemented, is a very strong baseline. We release our modular and extensible codebase at https://github.com/AlbinSou/ocl_survey based on the avalanche framework to reproduce our results and encourage future research.