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.
|Conference||2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops|
|Abbreviated title||CVPRW 2021|
|Period||19/06/21 → 25/06/21|
- Electrical and Electronic Engineering
- Computer Vision and Pattern Recognition