Exploration and exploitation in continual learning.

Area of Science:

• Artificial Intelligence
• Machine Learning

Background:

• Continual learning (CL) aims to prevent catastrophic forgetting using parameter isolation.
• Existing methods often use fixed parameter ratios, ignoring task-specific needs and leading to suboptimal performance.

Purpose of the Study:

• To develop a novel Exploration-Exploitation approach for adaptive parameter allocation in continual learning.
• To enable continual learners to dynamically balance acquiring new information (Exploration) and retaining old knowledge (Exploitation).

Main Methods:

• Introduced an adaptive Exploration-Exploitation strategy for resource distribution in continual learning.
• Developed an allocation learner to manage the interplay between exploration and exploitation across all layers.
• Evaluated the method on image classification benchmarks in diverse CL scenarios, including domain-shift task-incremental learning.

Main Results:

• The proposed Exploration-Exploitation method adaptively allocates parameters for each new task.
• Demonstrated superior performance compared to existing continual learning approaches.
• Achieved an average performance improvement of 5.3% across various scenarios.

Conclusions:

• Adaptive parameter allocation using Exploration-Exploitation is effective for continual learning.
• The proposed method addresses limitations of fixed parameter ratio approaches.
• This work advances continual learning by optimizing resource management for diverse tasks.