@InProceedings{VincenzoLomonaco2021,
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 van de Ven
and Martin Mundt
and Qi She
and Keiland Cooper
and Jeremy Forest
and Eden Belouadah
and Simone Calderara
and German I. Parisi
and Fabio Cuzzolin
and Andreas Tolias
and Simone Scardapane
and Luca Antiga
and Subutai Amhad
and Adrian Popescu
and Christopher Kanan
and Joost Van de Weijer
and Tinne Tuytelaars
and Davide Bacciu
and Davide Maltoni",
title="Avalanche: an End-to-End Library for Continual Learning",
booktitle="34th IEEE Conference on Computer Vision and Pattern Recognition Workshops",
year="2021",
pages="3595--3605",
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.",
optnote="LAMP; 600.120",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3567), last updated on Mon, 24 Oct 2022 15:46:27 +0200",
doi="10.1109/CVPRW53098.2021.00399",
file=":http://refbase.cvc.uab.es/files/LPC2021.pdf:PDF"
}