Avalanche: an End-to-End Library for Continual Learning

Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing interest in continual learning, especially within the deep learning community. However, algorithmic solutions are often...

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Published inarXiv.org
Main Authors Lomonaco, Vincenzo, Pellegrini, Lorenzo, Cossu, Andrea, Carta, Antonio, Graffieti, Gabriele, Hayes, Tyler L, De Lange, Matthias, Masana, Marc, Pomponi, Jary, Gido van de Ven, Mundt, Martin, She, Qi, Cooper, Keiland, est, Jeremy, Eden Belouadah, Calderara, Simone, Parisi, German I, Cuzzolin, Fabio, Tolias, Andreas, Scardapane, Simone, Antiga, Luca, Amhad, Subutai, Popescu, Adrian, Kanan, Christopher, van de Weijer, Joost, Tuytelaars, Tinne, Bacciu, Davide, Maltoni, Davide
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 01.04.2021
Subjects
Algorithms
Avalanches
Data transmission
Initiatives
Libraries
Machine learning
Prototyping
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Summary:Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing interest in continual learning, especially within the deep learning community. However, algorithmic solutions are often difficult to re-implement, evaluate and port across different settings, where even results on standard benchmarks are hard to reproduce. In this work, we propose Avalanche, an open-source end-to-end library for continual learning research based on PyTorch. Avalanche is designed to provide a shared and collaborative codebase for fast prototyping, training, and reproducible evaluation of continual learning algorithms.
ISSN:2331-8422