Causal Variational Autoencoder (Causal VAE)

This repository implements a Causal Variational Autoencoder (Causal VAE) with a focus on adaptability to different resource constraints and datasets. It is heavily influenced by Huawei Noah's Ark Causal VAE, with adaptations made to suit our specific needs.

Features

• Implementation of a Causal VAE
• Adaptations for varying resource constraints (memory, GPU, disk storage)
• Support for multiple datasets: Flow and Pendulum

Usage

To utilize this repository, follow these steps:

1. Clone the Repository:

   git clone https://github.com/your-username/your-repository.git
   cd your-repository

2. Training the Model:

   • To train the model on a specific dataset (e.g., Flow), run:

     python run_flow.py

3. Inference:

   • After training, you can perform inference using:

     python inference_flow.py

Datasets

This repository includes two datasets:

• Flow
• Pendulum

Note

During both training and inference, the code will generate plots and store them in designated folders.

Acknowledgements

This repository is heavily influenced by Huawei Noah's Ark Causal VAE.

License

This project is licensed under the MIT License - see the LICENSE file for details.