TY  - CONF
AU  - Vincenzo Lomonaco
AU  - Lorenzo Pellegrini
AU  - Andrea Cossu
AU  - Antonio Carta
AU  - Gabriele Graffieti
AU  - Tyler L. Hayes
AU  - Matthias De Lange
AU  - Marc Masana
AU  - Jary Pomponi
AU  - Gido van de Ven
AU  - Martin Mundt
AU  - Qi She
AU  - Keiland Cooper
AU  - Jeremy Forest
AU  - Eden Belouadah
AU  - Simone Calderara
AU  - German I. Parisi
AU  - Fabio Cuzzolin
AU  - Andreas Tolias
AU  - Simone Scardapane
AU  - Luca Antiga
AU  - Subutai Amhad
AU  - Adrian Popescu
AU  - Christopher Kanan
AU  - Joost Van de Weijer
AU  - Tinne Tuytelaars
AU  - Davide Bacciu
AU  - Davide Maltoni
A2  - CVPRW
PY  - 2021//
TI  - Avalanche: an End-to-End Library for Continual Learning
BT  - 34th IEEE Conference on Computer Vision and Pattern Recognition Workshops
SP  - 3595
EP  - 3605
N2  - 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.
L1  - http://refbase.cvc.uab.es/files/LPC2021.pdf
UR  - http://dx.doi.org/10.1109/CVPRW53098.2021.00399
N1  - LAMP; 600.120
ID  - Vincenzo Lomonaco2021
ER  -