Avalanche: an End-to-End Library for Continual Learning (2104.00405v1)

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.

• The paper introduces Avalanche, a comprehensive PyTorch library designed to standardize and accelerate research in continual learning by providing tools for benchmarks, training, and evaluation.
• Avalanche includes five core modules: Benchmarks for diverse datasets, Training strategies with plugins, Evaluation metrics, Logging formats, and pre-implemented Models.
• This library aims to improve reproducibility and comparability in CL research, primarily focusing on supervised vision tasks, with potential for future expansion into other learning paradigms.

Overview of "Avalanche: an End-to-End Library for Continual Learning"

The paper "Avalanche: an End-to-End Library for Continual Learning" introduces Avalanche, a comprehensive, open-source library tailored for continual learning (CL) research. Built upon PyTorch, Avalanche aims to facilitate the reproducibility and comparability of CL algorithms by providing a standardized interface for building and testing CL systems. This contribution is particularly valuable given the increasing complexity and diversity associated with continual learning methods.

Continual learning, also known as incremental or lifelong learning, involves developing algorithms that can learn continuously from non-stationary data streams. The diversity of applications and rapid development within the field presents challenges in terms of algorithm evaluation and implementation. Avalanche seeks to mitigate these challenges by providing a shared codebase that enables researchers to conduct fast prototyping, consistent training, and reproducible evaluation of CL models.

Key Features and Components

Avalanche is organized into five primary modules, each crucial for defining and evaluating CL strategies:

1. Benchmarks Module: This module offers a versatile set of datasets and benchmarks designed for various scenarios such as multi-task, class-incremental, domain-incremental, and more. It also supports custom benchmarks, thus accommodating a broad spectrum of continual learning paradigms. Avalanche emphasizes minimizing assumptions to ensure flexibility across different CL contexts.
2. Training Module: The training module presents an extensible set of baseline strategies, embedding a plugin system that allows custom modifications and hybrid approaches. This modular architecture supports efficient implementation and experimentation of various CL algorithms, like EWC and GEM, through its robust structure.
3. Evaluation Module: A key challenge in CL research is effective evaluation. The evaluation module offers a comprehensive set of metrics covering accuracy, forgetting, resource usage, and computational costs. This modularity ensures that Avalanche can cater to various evaluation needs across distinct learning scenarios, facilitating broader and rigorous experimental assessments.
4. Logging Module: This module supports multiple logging formats including text, interactive logs, and Tensorboard visualizations, enabling researchers to monitor experiments in real-time and document metrics comprehensively. Such detailed logging is essential for experiment replication and incremental research advancements.
5. Models Module: Providing pre-implemented machine learning architectures, this module aims to help researchers focus on CL strategies rather than model engineering. It includes standard neural network architectures ready for deployment in CL environments.

Implications and Future Directions

Avalanche represents a significant step toward standardizing continual learning research practices. By leveraging a comprehensive library, researchers can focus on advancing CL methodologies without expending resources on overcoming logistical and comparability hurdles typically associated with bespoke implementations. This potentially accelerates theoretical advancements and facilitates the integration of CL across various domains such as robotics, computer vision, and natural language processing.

However, the library's current focus is predominantly on supervised learning in the vision domain. Expanding its capabilities to include reinforcement learning, unsupervised learning, and other applications will be necessary to ensure its relevance and utility as the field evolves. Future updates must also consider scalability to cover larger datasets and more complex architectures beyond current offerings.

Conclusion

Avalanche aims to foster a collaborative environment within the continual learning community by providing a shared codebase that elevates transparency, reproducibility, and scalability in research practices. By easing the burden of implementation and standardization, Avalanche could shape future developments in continual learning, potentially enabling novel applications and accelerating the field’s evolution.