Transfer Learning With Singular Value Decomposition of Multichannel Convolution Matrices.

Area of Science:

• Computer Science
• Machine Learning
• Artificial Intelligence

Background:

• Transfer learning leverages pretrained models to improve performance on new tasks.
• Convolutional Neural Networks (CNNs) are powerful tools for image analysis but can be prone to overfitting.
• Analyzing convolution operators is key to understanding CNN behavior.

Purpose of the Study:

• To propose a novel convolution-SVD layer for analyzing convolution operators in transfer learning.
• To achieve dimension reduction and avoid overfitting while maintaining flexibility in fine-tuning.
• To develop a regularization model based on the transfer learning gap.

Main Methods:

• Singular Value Decomposition (SVD) computed in the Fourier domain for convolution operators.
• Transferring singular vectors from source to target domains and fine-tuning singular values.
• Extending convolution kernel reconstruction algorithms and devising generalization bounds.
• Introducing and utilizing a transfer learning gap as a regularizer.

Main Results:

• The proposed convolution-SVD layer achieves dimension reduction, preventing overfitting.
• A generalization bound demonstrates consistency between training and testing errors.
• The regularization model effectively bounds testing error using the transfer learning gap.
• Numerical experiments show superior performance in classification tasks.

Conclusions:

• The convolution-SVD layer offers an effective approach for transfer learning in CNNs.
• Regularization based on the transfer learning gap significantly improves prediction accuracy.
• The method provides a balance between dimension reduction and model flexibility.