Avalanche: an End-to-End Library for Continual Learning
This addresses the need for standardized tools to facilitate collaborative and reproducible research in continual learning, particularly for the deep learning community.
The authors tackled the problem of difficult re-implementation, evaluation, and reproducibility in continual learning research by proposing Avalanche, an open-source end-to-end library based on PyTorch, which provides a shared codebase for fast prototyping, training, and reproducible evaluation.
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.