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

@article{Lomonaco2021AvalancheAE,
  title={Avalanche: an End-to-End Library for Continual Learning},
  author={Vincenzo Lomonaco and Lorenzo Pellegrini and Andrea Cossu and Antonio Carta and Gabriele Graffieti and Tyler L. Hayes and Matthias De Lange and Marc Masana and Jary Pomponi and Gido M. van de Ven and Martin Mundt and Qi She and Keiland W Cooper and Jeremy Forest and Eden Belouadah and Simone Calderara and German Ignacio Parisi and Fabio Cuzzolin and Andreas Savas Tolias and Simone Scardapane and Luca Antiga and Subutai Amhad and Adrian Daniel Popescu and Christopher Kanan and Joost van de Weijer and Tinne Tuytelaars and Davide Bacciu and Davide Maltoni},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  year={2021},
  pages={3595-3605}
}
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… 

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