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, VincenzoPellegrini, LorenzoCossu, AndreaCarta, AntonioGraffieti, GabrieleHayes, Tyler L.De Lange, MatthiasMasana, MarcPomponi, Jaryvan de Ven, Gido M.Mundt, MartinShe, QiCooper, KeilandForest, JeremyBelouadah, EdenCalderara, Simone Parisi, German I.Cuzzolin, FabioTolias, Andreas S.Scardapane, SimoneAntiga, LucaAhmad, SubutaiPopescu, AdrianKanan, Christophervan de Weijer, JoostTuytelaars, TinneBacciu, DavideMaltoni, Davide
School of Engineering, Computing and Mathematics
Year of publication: 2021Date of RADAR deposit: 2021-10-25
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