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deep continual learning

Deep continual learning is a subfield of artificial intelligence focused on training deep neural networks to sequentially acquire, update, and accumulate knowledge over time from continuous, nonstationary streams of data. Unlike standard deep learning approaches that rely on static datasets and offline training, deep continual learning aims to adapt models to new tasks and changing environments without suffering from catastrophic forgetting, a phenomenon where newly learned information overwrites previously acquired capabilities. To achieve this, methods in deep continual learning manage the trade-off between plasticity, the ability to integrate new information, and stability, the capacity to preserve past knowledge, utilizing strategies such as memory replay, architectural expansion, and parameter regularization without requiring complete retraining from scratch.

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Avalanche: A PyTorch Library for Deep Continual Learning

Avalanche: A PyTorch Library for Deep Continual Learning

Antonio Carta, Lorenzo Pellegrini, Andrea Cossu, Hamed Hemati, Vincenzo Lomonaco

OrganizationsScuola Normale SuperioreUniversity of BolognaUniversity of PisaUniversity of St. Gallen

Why you should read this

Introduces Avalanche, an open-source PyTorch library that simplifies continual learning research by providing dynamic neural network architectures, streaming data loaders, modular training plugins, and standardized evaluation benchmarks.

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 https://avalanche.continualai.org.

Added

2026-10-04