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.
Originalsprache | englisch |
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Titel | Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 |
Seiten | 3595-3605 |
Seitenumfang | 11 |
ISBN (elektronisch) | 9781665448994 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2021 |
Extern publiziert | Ja |
Veranstaltung | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops: CVPRW 2021 - Virtuell, USA / Vereinigte Staaten Dauer: 19 Juni 2021 → 25 Juni 2021 |
Konferenz
Konferenz | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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Kurztitel | CVPRW 2021 |
Land/Gebiet | USA / Vereinigte Staaten |
Ort | Virtuell |
Zeitraum | 19/06/21 → 25/06/21 |
ASJC Scopus subject areas
- Elektrotechnik und Elektronik
- Maschinelles Sehen und Mustererkennung