Incremental Learning Through Deep Adaptation.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Continual learning in neural networks aims to acquire new capabilities without compromising existing knowledge.
• Current methods often lead to suboptimal performance, require extensive joint training, or drastically increase model size for each new domain.

Purpose of the Study:

• To introduce a novel method, Deep Adaptation Modules (DAM), for efficient and effective continual learning in neural networks.
• To enable a single neural network to adapt to multiple domains without performance degradation on previously learned tasks.

Main Methods:

• Proposes Deep Adaptation Modules (DAM) that constrain new filters to be linear combinations of existing ones.
• Integrates DAM with standard network quantization techniques to further reduce parameter count.
• Evaluates the method on various image classification tasks.

Main Results:

• DAM preserves performance on original domains while requiring only a fraction (typically 13%) of the parameters of standard fine-tuning.
• Combined with quantization, parameter cost is reduced to approximately 3% with minimal accuracy loss.
• Achieves comparable or superior performance with fewer training cycles.

Conclusions:

• Deep Adaptation Modules offer an efficient solution for continual learning, significantly reducing computational and memory overhead.
• The adaptable architecture allows a single network to master diverse tasks from multiple domains.
• DAM presents a promising approach for developing more versatile and scalable neural network models.