🚧 This Repository is Under Construction 🚧
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.
- 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.
- 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.
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.