# Avalanche: A PyTorch Library for Deep Continual Learning

arXiv: https://arxiv.org/abs/2302.01766 · HTML: https://mlsanity.com/paper/2302.01766/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 10 references that are in the map.
- First posted on arXiv 2 Feb 2023.

## Builds on (most cited first, 7 of 7)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Class-incremental learning: survey and performance evaluation on image classification](https://mlsanity.com/paper/2010.15277/) (2020)
- [Avalanche: an End-to-End Library for Continual Learning](https://mlsanity.com/paper/2104.00405/) (2021)
- [Continual World: A Robotic Benchmark For Continual Reinforcement Learning](https://mlsanity.com/paper/2105.10919/) (2021)
- [Continuum: Simple Management of Complex Continual Learning Scenarios](https://mlsanity.com/paper/2102.06253/) (2021)
- [Sequoia: A Software Framework to Unify Continual Learning Research](https://mlsanity.com/paper/2108.01005/) (2021)

## Built on by (most cited first, 30 of 33)

- [On Efficient Training of Large-Scale Deep Learning Models: A Literature Review](https://mlsanity.com/paper/2304.03589/) (2023)
- [A Comprehensive Empirical Evaluation on Online Continual Learning](https://mlsanity.com/paper/2308.10328/) (2023)
- [Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning](https://mlsanity.com/paper/2402.09542/) (2024)
- [On the Computation of the Fisher Information in Continual Learning](https://mlsanity.com/paper/2502.11756/) (2025)
- [MCITlib: Multimodal Continual Instruction Tuning Library and Benchmark](https://mlsanity.com/paper/2508.07307/) (2025)
- [Continual Learning in the Presence of Repetition](https://mlsanity.com/paper/2405.04101/) (2024)
- [Class-incremental Learning for Time Series: Benchmark and Evaluation](https://mlsanity.com/paper/2402.12035/) (2024)
- [Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications](https://mlsanity.com/paper/2410.09218/) (2024)
- [Online Curvature-Aware Replay: Leveraging mathbf2nd Order Information for Online Continual Learning](https://mlsanity.com/paper/2502.01866/) (2025)
- [Calibration of Continual Learning Models](https://mlsanity.com/paper/2404.07817/) (2024)
- [An Empirical Analysis of Forgetting in Pre-trained Models with Incremental Low-Rank Updates](https://mlsanity.com/paper/2405.18069/) (2024)
- [Overcoming Catastrophic Forgetting in Tabular Data Classification: A Pseudorehearsal-based approach](https://mlsanity.com/paper/2407.09039/) (2024)
- [On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients](https://mlsanity.com/paper/2411.07959/) (2024)
- [Active Learning for Continual Learning: Keeping the Past Alive in the Present](https://mlsanity.com/paper/2501.14278/) (2025)
- [CODE-CL: Conceptor-Based Gradient Projection for Deep Continual Learning](https://mlsanity.com/paper/2411.15235/) (2024)
- [A Protocol for Continual Explanation of SHAP](https://mlsanity.com/paper/2306.07218/) (2023)
- [Keep Moving: identifying task-relevant subspaces to maximise plasticity for newly learned tasks](https://mlsanity.com/paper/2310.04741/) (2023)
- [RECAST: Reparameterized, Compact weight Adaptation for Sequential Tasks](https://mlsanity.com/paper/2411.16870/) (2024)
- [CapyMOA: Efficient Machine Learning for Data Streams and Online Continual Learning in Python](https://mlsanity.com/paper/2502.07432/) (2025)
- [Regression-aware Continual Learning for Android Malware Detection](https://mlsanity.com/paper/2507.18313/) (2025)
- [A Practical Guide to Streaming Continual Learning](https://mlsanity.com/paper/2603.01677/) (2026)
- [Sample Compression for Self Certified Continual Learning](https://mlsanity.com/paper/2503.10503/) (2025)
- [Compact Memory for Continual Logistic Regression](https://mlsanity.com/paper/2511.09167/) (2025)
- [Mitigating Forgetting in Continual Learning with Selective Gradient Projection](https://mlsanity.com/paper/2603.26671/) (2026)
- [Sample Weight Estimation Using Meta-Updates for Online Continual Learning](https://mlsanity.com/paper/2401.15973/) (2024)
- [Continual Learning by Three-Phase Consolidation](https://mlsanity.com/paper/2403.14679/) (2024)
- [Examining Changes in Internal Representations of Continual Learning Models Through Tensor Decomposition](https://mlsanity.com/paper/2405.03244/) (2024)
- [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)
- [Noise-Tolerant Coreset-Based Class Incremental Continual Learning](https://mlsanity.com/paper/2504.16763/) (2025)
- [Exploring Cross-Stage Adversarial Transferability in Class-Incremental Continual Learning](https://mlsanity.com/paper/2508.08920/) (2025)

## Abstract (arXiv)

Continual learning is the problem of learning from a nonstationary stream of data, a fundamental issue for sustainable and efficient training of deep neural networks over time. Unfortunately, deep learning libraries only provide primitives for offline training, assuming that model's architecture and data are fixed. Avalanche is an open source library maintained by the ContinualAI non-profit organization that extends PyTorch by providing first-class support for dynamic architectures, streams of datasets, and incremental training and evaluation methods. Avalanche provides a large set of predefined benchmarks and training algorithms and it is easy to extend and modular while supporting a wide range of continual learning scenarios. Documentation is available at urlhttps://avalanche.continualai.org.