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A Comprehensive Survey of Continual Learning: Theory, Method and Application
IEEE Transactions on Pattern Analysis and Machine Intelligence
|February 26, 2024
Summary
Continual learning enables AI systems to adapt by acquiring knowledge over time. This survey addresses catastrophic forgetting and explores methods for stable, adaptable AI in real-world applications.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Computer Science
Background:
- Intelligent systems require lifelong knowledge acquisition and updating for real-world dynamics.
- Continual learning is crucial for adaptive AI development but is challenged by catastrophic forgetting.
- Recent advances have significantly expanded the understanding and application of continual learning.
Purpose of the Study:
- To provide a comprehensive survey of continual learning.
- To bridge basic settings, theoretical foundations, representative methods, and practical applications.
- To facilitate future research in continual learning.
Main Methods:
- Summarizing general objectives: stability-plasticity trade-off and intra/inter-task generalizability.
- Presenting a state-of-the-art taxonomy of continual learning strategies.
- Analyzing how methods address challenges in various applications.
Main Results:
- Continual learning aims for a balance between retaining old knowledge and acquiring new information.
- A structured taxonomy categorizes and analyzes diverse continual learning approaches.
- Methods are evaluated for their effectiveness in different application contexts.
Conclusions:
- A holistic perspective on continual learning is essential for advancing the field.
- Addressing catastrophic forgetting and ensuring generalizability are key objectives.
- This survey provides a foundation for future exploration in adaptive AI systems.
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