Adaptive Progressive Continual Learning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Deep Learning

Background:

• Continual learning aims to incrementally learn from a stream of tasks, addressing the challenge of catastrophic forgetting.
• Existing methods often struggle to balance learning new information with retaining previously acquired knowledge.

Purpose of the Study:

• To propose a novel adaptive progressive network framework for continual learning.
• To introduce two models, Reinforced Continual Learning (RCL) and Bayesian Optimized Continual Learning (BOCL), to prevent catastrophic forgetting.
• To dynamically adapt neural network architecture for efficient knowledge retention and acquisition.

Main Methods:

• Developed an adaptive progressive network framework that dynamically expands neural network structures for new tasks.
• Utilized reinforcement learning in Reinforced Continual Learning (RCL) and Bayesian optimization with an attention mechanism in BOCL.
• Implemented strategies for adaptive architecture control, enabling selective utilization of previously learned knowledge.

Main Results:

• The proposed framework effectively prevents catastrophic forgetting by adaptively managing network architecture.
• Experiments on MNIST, CIFAR-100, and a sequence of datasets demonstrated superior performance over state-of-the-art methods.
• Achieved better performance in fitting new tasks while maintaining previous knowledge, using comparable or fewer computational resources.

Conclusions:

• The adaptive progressive network framework, incorporating RCL and BOCL, offers a robust solution to catastrophic forgetting in continual learning.
• Dynamic network expansion and selective knowledge utilization are key to achieving efficient and stable incremental learning.
• The proposed methods represent a significant advancement in continual learning, enhancing model adaptability and knowledge preservation.