# Is Class-Incremental Enough for Continual Learning?

arXiv: https://arxiv.org/abs/2112.02925 · HTML: https://mlsanity.com/paper/2112.02925/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 23 references that are in the map.
- First posted on arXiv 6 Dec 2021.

## Builds on (most cited first, 17 of 17)

- [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)
- [Three scenarios for continual learning](https://mlsanity.com/paper/1904.07734/) (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)
- [Task-Free Continual Learning](https://mlsanity.com/paper/1812.03596/) (2018)
- [Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams](https://mlsanity.com/paper/2009.00919/) (2020)
- [Avalanche: an End-to-End Library for Continual Learning](https://mlsanity.com/paper/2104.00405/) (2021)
- [Don't forget, there is more than forgetting: new metrics for Continual Learning](https://mlsanity.com/paper/1810.13166/) (2018)
- [Continuous Learning in Single-Incremental-Task Scenarios](https://mlsanity.com/paper/1806.08568/) (2018)
- [Continual Learning for Robotics: Definition, Framework, Learning Strategies, Opportunities and Challenges](https://mlsanity.com/paper/1907.00182/) (2019)
- [Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches](https://mlsanity.com/paper/1907.03799/) (2019)
- [Continual Learning for Recurrent Neural Networks: an Empirical Evaluation](https://mlsanity.com/paper/2103.07492/) (2021)
- [Semi-supervised Tuning from Temporal Coherence](https://mlsanity.com/paper/1511.03163/) (2015)

## Built on by (most cited first, 22 of 22)

- [TiC-CLIP: Continual Training of CLIP Models](https://mlsanity.com/paper/2310.16226/) (2023)
- [CLAD: A realistic Continual Learning benchmark for Autonomous Driving](https://mlsanity.com/paper/2210.03482/) (2022)
- [Continual Learning in Linear Classification on Separable Data](https://mlsanity.com/paper/2306.03534/) (2023)
- [Challenging Common Assumptions about Catastrophic Forgetting](https://mlsanity.com/paper/2207.04543/) (2022)
- [Online Class-Incremental Learning For Real-World Food Image Classification](https://mlsanity.com/paper/2301.05246/) (2023)
- [A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems](https://mlsanity.com/paper/2301.07799/) (2023)
- [Continual Learning for Predictive Maintenance: Overview and Challenges](https://mlsanity.com/paper/2301.12467/) (2023)
- [Class-Incremental Learning with Repetition](https://mlsanity.com/paper/2301.11396/) (2023)
- [Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights](https://mlsanity.com/paper/2303.07557/) (2023)
- [One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning](https://mlsanity.com/paper/2311.12048/) (2023)
- [From MNIST to ImageNet and Back: Benchmarking Continual Curriculum Learning](https://mlsanity.com/paper/2303.11076/) (2023)
- [Heterogeneous Continual Learning](https://mlsanity.com/paper/2306.08593/) (2023)
- [Class-Prototype Conditional Diffusion Model with Gradient Projection for Continual Learning](https://mlsanity.com/paper/2312.06710/) (2023)
- [Holistic Continual Learning under Concept Drift with Adaptive Memory Realignment](https://mlsanity.com/paper/2507.02310/) (2025)
- [Data Heterogeneity and Forgotten Labels in Split Federated Learning](https://mlsanity.com/paper/2511.09736/) (2025)
- [Architect, Regularize and Replay (ARR): a Flexible Hybrid Approach for Continual Learning](https://mlsanity.com/paper/2301.02464/) (2023)
- [Ada-QPacknet -- adaptive pruning with bit width reduction as an efficient continual learning method without forgetting](https://mlsanity.com/paper/2308.07939/) (2023)
- [Incremental Learning with Repetition via Pseudo-Feature Projection](https://mlsanity.com/paper/2502.19922/) (2025)
- [Lifelong Evolution of Swarms](https://mlsanity.com/paper/2503.17763/) (2025)
- [Exploring Kolmogorov-Arnold Network Expansions in Vision Transformers for Mitigating Catastrophic Forgetting in Continual Learning](https://mlsanity.com/paper/2507.04020/) (2025)
- [Negotiated Representations to Prevent Forgetting in Machine Learning Applications](https://mlsanity.com/paper/2312.00237/) (2023)
- [Amnesia: A Stealthy Replay Attack on Continual Learning Dreams](https://mlsanity.com/paper/2606.12655/) (2026)

## Abstract (arXiv)

The ability of a model to learn continually can be empirically assessed in different continual learning scenarios. Each scenario defines the constraints and the opportunities of the learning environment. Here, we challenge the current trend in the continual learning literature to experiment mainly on class-incremental scenarios, where classes present in one experience are never revisited. We posit that an excessive focus on this setting may be limiting for future research on continual learning, since class-incremental scenarios artificially exacerbate catastrophic forgetting, at the expense of other important objectives like forward transfer and computational efficiency. In many real-world environments, in fact, repetition of previously encountered concepts occurs naturally and contributes to softening the disruption of previous knowledge. We advocate for a more in-depth study of alternative continual learning scenarios, in which repetition is integrated by design in the stream of incoming information. Starting from already existing proposals, we describe the advantages such class-incremental with repetition scenarios could offer for a more comprehensive assessment of continual learning models.