Conference Paper


Avalanche: An end-to-end library for continual learning

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.

Attached files

Authors

Lomonaco, Vincenzo
Pellegrini, Lorenzo
Cossu, Andrea
Carta, Antonio
Graffieti, Gabriele
Hayes, Tyler L.
De Lange, Matthias
Masana, Marc
Pomponi, Jary
van de Ven, Gido M.
Mundt, Martin
She, Qi
Cooper, Keiland
Forest, Jeremy
Belouadah, Eden
Calderara, Simone
Parisi, German I.
Cuzzolin, Fabio
Tolias, Andreas S.
Scardapane, Simone
Antiga, Luca
Ahmad, Subutai
Popescu, Adrian
Kanan, Christopher
van de Weijer, Joost
Tuytelaars, Tinne
Bacciu, Davide
Maltoni, Davide

Oxford Brookes departments

School of Engineering, Computing and Mathematics

Dates

Year of publication: 2021
Date of RADAR deposit: 2021-10-25



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Related resources

This RADAR resource is Identical to Avalanche: An end-to-end library for continual learning
This RADAR resource is the Accepted Manuscript of Avalanche: An end-to-end library for continual learning
This RADAR resource is Identical to [arXiv preprint:] Avalanche: An end-to-end library for continual learning

Details

  • Owner: Daniel Croft
  • Collection: Outputs
  • Version: 1 (show all)
  • Status: Live