# A Comprehensive Survey of Continual Learning: Theory, Method and Application

**Authors:** Liyuan Wang, Xingxing Zhang, Hang Su, Jun Zhu  
**arXiv:** [2302.00487](https://arxiv.org/abs/2302.00487)  
**Date:** 2023-01-31  
**Summary page:** https://www.summarizepaper.com/en/paper/2302.00487v1/

## Key points

- Continual learning is essential for intelligent agents to adapt to real-world dynamics effectively
- Catastrophic forgetting is a challenge in continual learning where learning new tasks can lead to a decline in performance on previously learned tasks
- Recent advancements have expanded the understanding and application of continual learning
- The paper by Wang et al. aims to bridge fundamental settings, theoretical foundations, methods, and applications in continual learning
- Primary objectives of continual learning include balancing stability and plasticity while ensuring generalizability within resource-efficient constraints
- The paper provides a taxonomy that examines how different strategies address challenges in continual learning across various applications
- Interdisciplinary connections with neuroscience can enhance exploration in the field of continual learning

## Summary

In their paper titled "A Comprehensive Survey of Continual Learning: Theory, Method and Application," authors Liyuan Wang, Xingxing Zhang, Hang Su, and Jun Zhu delve into the critical concept of continual learning in the realm of artificial intelligence. They highlight the necessity for intelligent agents to continuously acquire, update, accumulate, and leverage knowledge over their lifespan to adapt to real-world dynamics effectively. This process of continual learning serves as a cornerstone for AI systems to evolve autonomously. The authors emphasize that while continual learning is crucial for AI advancement, it is often hindered by catastrophic forgetting. This phenomenon occurs when learning a new task leads to a significant decline in performance on previously learned tasks. Despite this challenge, recent years have witnessed a surge in advancements that have expanded the understanding and application of continual learning. In response to this growing interest and complexity surrounding continual learning, Wang et al. present a comprehensive survey aimed at bridging fundamental settings, theoretical foundations, representative methods, and practical applications in this field. Drawing from existing theoretical frameworks and empirical findings, they outline the primary objectives of continual learning as striking a balance between stability and plasticity while ensuring intra/inter-task generalizability within resource-efficient constraints. The paper offers an elaborate taxonomy that thoroughly examines how various strategies address the challenges posed by continual learning across different applications. By providing insights into current trends and interdisciplinary connections with neuroscience,the authors believe that adopting a holistic perspective can significantly enhance exploration in this field and beyond. Through their detailed analysis and discussion,Wang et al.contribute valuable insights to furthering research in continual learning within the realm of artificial intelligence.