Sequoia: A Software Framework to Unify Continual Learning Research (2108.01005v4)

Abstract: The field of Continual Learning (CL) seeks to develop algorithms that accumulate knowledge and skills over time through interaction with non-stationary environments. In practice, a plethora of evaluation procedures (settings) and algorithmic solutions (methods) exist, each with their own potentially disjoint set of assumptions. This variety makes measuring progress in CL difficult. We propose a taxonomy of settings, where each setting is described as a set of assumptions. A tree-shaped hierarchy emerges from this view, where more general settings become the parents of those with more restrictive assumptions. This makes it possible to use inheritance to share and reuse research, as developing a method for a given setting also makes it directly applicable onto any of its children. We instantiate this idea as a publicly available software framework called Sequoia, which features a wide variety of settings from both the Continual Supervised Learning (CSL) and Continual Reinforcement Learning (CRL) domains. Sequoia also includes a growing suite of methods which are easy to extend and customize, in addition to more specialized methods from external libraries. We hope that this new paradigm and its first implementation can help unify and accelerate research in CL. You can help us grow the tree by visiting www.github.com/lebrice/Sequoia.

• The paper introduces Sequoia as a unified framework that streamlines continual learning research by organizing settings into a hierarchical structure.
• It standardizes evaluation metrics such as transfer, forward/backward transfer, and online and final performance across CSL and CRL paradigms.
• Empirical results on datasets like MNIST and CIFAR10 demonstrate its effectiveness in reducing redundant efforts and enhancing algorithm reusability.

Unifying Continual Learning with the Sequoia Framework: A Comprehensive Overview

The paper "Sequoia: A Software Framework to Unify Continual Learning Research" addresses the challenges inherent in the diverse field of Continual Learning (CL) by proposing a novel framework designed to standardize and unify research efforts across the domain. The burgeoning field of CL seeks to design algorithms capable of learning continuously from changing data distributions, a task complicated by the diversity of assumptions and methods across different research efforts. This paper's contribution, Sequoia, serves as a critical tool to streamline and synchronize CL research, providing a robust infrastructure for both Continual Supervised Learning (CSL) and Continual Reinforcement Learning (CRL) paradigms.

The Core Framework Concept

Sequoia introduces a hierarchical taxonomy wherein various CL settings are distilled into a coherent structure based on a set of assumptions. The framework's core innovation lies in organizing these settings as a tree, where more general environments (i.e., settings with fewer assumptions) are parent nodes to more specific ones. This organizational methodology facilitates the inheritance of research insights and methodologies across different settings, promoting easier reuse and adaptation of methods from one context to another within the CL spectrum.

The Sequoia Software Implementation

Operationalizing this conceptual hierarchy, Sequoia offers a versatile and extensible software platform. It provides a range of predefined settings covering the spectrum of both CSL and CRL. Using Sequoia, researchers can effortlessly extend and customize algorithmic methods and evaluation procedures. The software is constructed to boost both efficiency and reproducibility in CL research through its separation of concerns between evaluation frameworks (settings) and strategies (methods).

Metrics such as transfer, forward and backward transfer, final and online performance, are systematically evaluated, addressing the previously fragmented state of evaluation in CL. The framework aligns its methodology across the traditionally siloed CSL and CRL fields, advocating for a unified research front to mitigate duplicate efforts like replay mechanisms, which have historically developed in parallel for both domains.

Numerical Results and Empirical Evaluation

The paper demonstrates Sequoia's flexibility and utility through comprehensive empirical studies that cover CSL and CRL settings. These studies involve extensive hyper-parameter configurations across multiple datasets and environments, showcasing Sequoia's ability to handle diverse tasks. Notable results include strong performance demonstrated by specific methods, particularly in settings designed to assess both class-incremental and task-incremental learning using datasets such as MNIST, CIFAR10, and Cheetah-gravity, often demonstrating efficient trade-offs between online and final performance metrics.

Implications and Future Outlook

Sequoia's methodology has profound implications for CL. By providing a baseline for standardized evaluation and fostering method reusability across settings, Sequoia has the potential to significantly reduce the barriers to entry for new researchers and accelerate the development of innovative CL algorithms. While the current framework focuses primarily on CSL and CRL, its structure is adaptable, allowing future expansions to encompass unsupervised and semi-supervised continual learning contexts as research in these areas matures.

The framework’s emphasis on modularity and extensibility points toward a future where broadened collaboration across AI disciplines becomes feasible, allowing researchers to build upon and refine one another's work with increasing ease and precision.

In conclusion, Sequoia stands as a pivotal step in harmonizing continual learning research, offering a structured, comprehensive, and practical approach to evaluating and advancing algorithms in this critical and expanding field. As a publicly accessible resource, it invites the collective efforts of the AI research community to contribute to its continual evolution and to leverage its capabilities for breakthroughs in understanding and simulating learning processes over extended temporal scales.