math-modeling-pipeline/phase-5-dl

What problem does it solve?

Phase 5-DL addresses the need to optimize deep learning models within the math modeling pipeline by addressing cross-review issues, refining architecture, training schedules, and loss functions to improve objective metrics such as PSNR/SSIM.

Core Features & Use Cases

• Architecture optimization: add residual blocks, attention mechanisms, and multi-scale features to improve performance.
• Loss function enhancements: combine L1/Charbonnier/SSIM/perceptual losses to improve image quality.
• Training strategy improvements: advanced LR scheduling, data augmentation, mixed-precision training, EMA, and gradient accumulation for robust DL training.
• Workflow integration: supports cross-review feedback loops and end-to-end optimization within the 19-phase pipeline.

Quick Start

Initiate Phase 5-DL optimization by running the provided training scripts for your target path and feeding in the cross-review input data and audit results.