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Avalanche: An end-to-end library for continual learning

Abstract: 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.


Lomonaco, V.; Pellegrini, L.; Cossu, A.; Carta, A.; Graffieti, G.; Hayes, T. L.; De Lange, M.; Masana, M.; Pomponi, J.; Van De Ven, G. M.; Mundt, M.; She, Q.; Cooper, K.; Forest, J.; Belouadah, E.; Calderara, S.; Parisi, G. I.; Cuzzolin, F.; Tolias, A. S.; Scardapane, S.; Antiga, L.; Ahmad, S.; Popescu, A.; Kanan, C.; Van De Weijer, J.; Tuytelaars, T.; Bacciu, D.; Maltoni, D. "Avalanche: An end-to-end library for continual learning" IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, usa, pp. 3595 -3605 , 2021, 2021 DOI: 10.1109/CVPRW53098.2021.00399

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