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A Unified and General Framework for Continual Learning

Zhenyi Wang, Yan Li, Li Shen, Heng Huang

TL;DR

The proposed general framework introduces an innovative concept called refresh learning, specifically designed to enhance the CL performance, which serves as a versatile plug-in that seamlessly integrates with existing CL methods, offering an adaptable and effective enhancement to the learning process.

Abstract

Continual Learning (CL) focuses on learning from dynamic and changing data distributions while retaining previously acquired knowledge. Various methods have been developed to address the challenge of catastrophic forgetting, including regularization-based, Bayesian-based, and memory-replay-based techniques. However, these methods lack a unified framework and common terminology for describing their approaches. This research aims to bridge this gap by introducing a comprehensive and overarching framework that encompasses and reconciles these existing methodologies. Notably, this new framework is capable of encompassing established CL approaches as special instances within a unified and general optimization objective. An intriguing finding is that despite their diverse origins, these methods share common mathematical structures. This observation highlights the compatibility of these seemingly distinct techniques, revealing their interconnectedness through a shared underlying optimization objective. Moreover, the proposed general framework introduces an innovative concept called refresh learning, specifically designed to enhance the CL performance. This novel approach draws inspiration from neuroscience, where the human brain often sheds outdated information to improve the retention of crucial knowledge and facilitate the acquisition of new information. In essence, refresh learning operates by initially unlearning current data and subsequently relearning it. It serves as a versatile plug-in that seamlessly integrates with existing CL methods, offering an adaptable and effective enhancement to the learning process. Extensive experiments on CL benchmarks and theoretical analysis demonstrate the effectiveness of the proposed refresh learning. Code is available at \url{https://github.com/joey-wang123/CL-refresh-learning}.

A Unified and General Framework for Continual Learning

TL;DR

The proposed general framework introduces an innovative concept called refresh learning, specifically designed to enhance the CL performance, which serves as a versatile plug-in that seamlessly integrates with existing CL methods, offering an adaptable and effective enhancement to the learning process.

Abstract

Continual Learning (CL) focuses on learning from dynamic and changing data distributions while retaining previously acquired knowledge. Various methods have been developed to address the challenge of catastrophic forgetting, including regularization-based, Bayesian-based, and memory-replay-based techniques. However, these methods lack a unified framework and common terminology for describing their approaches. This research aims to bridge this gap by introducing a comprehensive and overarching framework that encompasses and reconciles these existing methodologies. Notably, this new framework is capable of encompassing established CL approaches as special instances within a unified and general optimization objective. An intriguing finding is that despite their diverse origins, these methods share common mathematical structures. This observation highlights the compatibility of these seemingly distinct techniques, revealing their interconnectedness through a shared underlying optimization objective. Moreover, the proposed general framework introduces an innovative concept called refresh learning, specifically designed to enhance the CL performance. This novel approach draws inspiration from neuroscience, where the human brain often sheds outdated information to improve the retention of crucial knowledge and facilitate the acquisition of new information. In essence, refresh learning operates by initially unlearning current data and subsequently relearning it. It serves as a versatile plug-in that seamlessly integrates with existing CL methods, offering an adaptable and effective enhancement to the learning process. Extensive experiments on CL benchmarks and theoretical analysis demonstrate the effectiveness of the proposed refresh learning. Code is available at \url{https://github.com/joey-wang123/CL-refresh-learning}.
Paper Structure (30 sections, 1 theorem, 44 equations, 10 tables, 1 algorithm)

This paper contains 30 sections, 1 theorem, 44 equations, 10 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Proposed Framework and Method
  4. Preliminary and Problem Setup
  5. A Unified and General Framework for CL
  6. Refresh Learning As a General Plug-in for CL
  7. Theoretical Analysis
  8. Experiments
  9. Setup
  10. Results
  11. Ablation Study and Hyperparameter Analysis
  12. Conclusion
  13. Acknowledgments
  14. Recast Existing CL Methods into Our Unified and General Framework
  15. Cast CPR into the general framework
  16. ...and 15 more sections

Key Result

Theorem 4.1

With one step of unlearning by Eq. (eq:unlearnprob), refresh learning approximately minimize the following FIM weighted gradient norm of the loss function. That is, solving Eq. (eq:relearn) and Eq. (eq:unlearnfunc) approximately solves the following optimization: where $\sigma>0$ is a constant.

Theorems & Definitions (2)

  • Theorem 4.1
  • proof