Boosting Learning Efficiency in Few-Shot Tasks With Layer-Adaptive PID Control

Area of Science:

• Machine Learning
• Artificial Intelligence
• Control Theory

Background:

• Few-shot learning aims to classify new categories with minimal data.
• Model-agnostic meta-learning (MAML) provides a flexible initialization for rapid adaptation.
• MAML struggles with significant distributional shifts, hindering generalization and efficient learning.

Purpose of the Study:

• To address the limitations of MAML in handling distributional shifts.
• To enhance the adaptation process in meta-learning beyond initialization.
• To improve few-shot learning performance across various tasks.

Main Methods:

• Proposed a novel Layer-Adaptive Proportional-Integral-Derivative (LA-PID) optimizer.
• Integrated LA-PID into a meta-learning framework.
• Applied principles from classical control theory (PID control) to dynamically adjust network layer gains.

Main Results:

• Achieved state-of-the-art performance on few-shot classification benchmarks.
• Demonstrated superior results in cross-domain few-shot learning tasks.
• Showcased effectiveness in few-shot regression tasks with fewer training steps.

Conclusions:

• LA-PID significantly improves adaptation capabilities in meta-learning.
• The proposed method overcomes MAML's limitations in distributional shifts.
• LA-PID offers a robust and efficient solution for data-scarce learning scenarios.