Computational Advantages of Multi-Grade Deep Learning: Convergence Analysis and Performance Insights
This work addresses performance and stability issues in deep learning for image processing, but it appears incremental as it builds on existing MGDL concepts with new analysis.
The paper tackled the computational advantages of multi-grade deep learning (MGDL) over single-grade deep learning (SGDL) in tasks like image regression, denoising, and deblurring, showing that MGDL is more robust to learning rate choices and has enhanced training stability.
Multi-grade deep learning (MGDL) has been shown to significantly outperform the standard single-grade deep learning (SGDL) across various applications. This work aims to investigate the computational advantages of MGDL focusing on its performance in image regression, denoising, and deblurring tasks, and comparing it to SGDL. We establish convergence results for the gradient descent (GD) method applied to these models and provide mathematical insights into MGDL's improved performance. In particular, we demonstrate that MGDL is more robust to the choice of learning rate under GD than SGDL. Furthermore, we analyze the eigenvalue distributions of the Jacobian matrices associated with the iterative schemes arising from the GD iterations, offering an explanation for MGDL's enhanced training stability.