Identity Mapping Module
🚧 This Repository is Under Construction 🚧
Overview
The Identity Mapping Module is a work-in-progress project focused on implementing identity initialization for deep learning models. By initializing models layer-by-layer with identity mappings, the project aims to enhance training stability, efficiency, and performance in complex neural networks.
Features (Planned)
- Identity initialization for all model layers.
- Compatibility with popular architectures like ResNet and Transformer-based models.
- Tools for experimenting with initialization methods based on information theory.
- Frameworks for testing on tasks like image inpainting and data compression while maintaining consistent information levels between input and output.
Objectives
- Streamline Training: Simplify the training of increasingly complex models by employing better initialization strategies.
- Information-Theoretic Insights: Explore how initialization influences the flow of information during training.
- Cross-Task Generalization: Validate the approach on multiple machine learning tasks to prove its versatility.
Current Status
This repository is still in the early development phase. Key components such as the implementation of identity-based initialization and experimental pipelines are under active construction.