PT Unknown
AU Vincenzo Lomonaco
   Lorenzo Pellegrini
   Andrea Cossu
   Antonio Carta
   Gabriele Graffieti
   Tyler L. Hayes
   Matthias De Lange
   Marc Masana
   Jary Pomponi
   Gido van de Ven
   Martin Mundt
   Qi She
   Keiland Cooper
   Jeremy Forest
   Eden Belouadah
   Simone Calderara
   German I. Parisi
   Fabio Cuzzolin
   Andreas Tolias
   Simone Scardapane
   Luca Antiga
   Subutai Amhad
   Adrian Popescu
   Christopher Kanan
   Joost Van de Weijer
   Tinne Tuytelaars
   Davide Bacciu
   Davide Maltoni
TI Avalanche: an End-to-End Library for Continual Learning
BT 34th IEEE Conference on Computer Vision and Pattern Recognition Workshops
PY 2021
BP 3595
EP 3605
DI 10.1109/CVPRW53098.2021.00399
AB 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.
ER