Fractal Few-Shot Learning.

Area of Science:

• Computer Science
• Machine Learning
• Artificial Intelligence

Background:

• Few-shot learning (FSL) relies on deep feature embeddings but struggles with task complexity and accuracy due to insufficient samples.
• Overcoming these limitations is crucial for advancing FSL applications.

Purpose of the Study:

• To propose a novel fractal embedding model that combines FSL with fractal dimension theory.
• To improve accuracy and robustness in FSL tasks, particularly with limited data.

Main Methods:

• Developed a fractal embedding model integrating FSL with fractal dimension theory.
• Improved a fractal dimension algorithm for neural network compatibility to describe image texture roughness.
• Incorporated prior knowledge from quantized images into features to mitigate data distribution effects.

Main Results:

• The proposed model demonstrated performance exceeding or matching state-of-the-art models on multiple image benchmark datasets.
• Achieved superior performance in cross-domain FSL scenarios, highlighting its robustness.
• Effectively reduced the impact of data distribution on model performance.

Conclusions:

• The fractal embedding model offers a promising approach for few-shot learning.
• The integration of fractal dimension theory and prior knowledge enhances FSL model accuracy and robustness.
• The model's cross-domain performance validates its generalization capabilities.