Dynamic transfer learning with progressive meta-task scheduler.

Area of Science:

• Machine Learning
• Artificial Intelligence
• Computer Science

Background:

• Dynamic transfer learning transfers knowledge from static to dynamic tasks with limited labels.
• Existing methods often assume continuous target task evolution, which is unrealistic.
• Sudden distribution shifts in target tasks pose a significant challenge.

Purpose of the Study:

• To propose a novel meta-learning framework, L2S, for dynamic transfer learning.
• To address the limitation of existing methods that assume continuous target task evolution.
• To enable fast adaptation to new target tasks, even with sudden distribution changes.

Main Methods:

• Introduced a meta-learning framework named L2S.
• Developed a progressive meta-task scheduler within L2S.
• Focused on incrementally learning to schedule meta-task pairs.
• Learned optimal model initialization from meta-pairs for rapid adaptation.

Main Results:

• The L2S framework demonstrated effectiveness in dynamic transfer learning.
• Theoretical validation confirmed the framework's capabilities.
• Empirical results supported the practical applicability and performance of L2S.
• Successfully adapted to target tasks with sudden distribution shifts.

Conclusions:

• L2S provides a robust solution for dynamic transfer learning with non-stationary target tasks.
• The proposed meta-learning approach enhances model adaptability.
• The framework's effectiveness is validated both theoretically and empirically.