Tensorflow Paper Implementations

Table of Contents

1. About The Project
2. Getting Started
   • Prerequisites
   • Installation
3. Contributing
4. License
5. Contact

About The Project

This project aims to provide open sourced Tensorflow implementations for research papers originally without code or code written with frameworks other than Tensorflow.
Available implementations for Tensorflow are:

1. Adaptive Fourier Neural Operators: Efficient Token Mixers For Transformers (John Guibas et al., 2021)
2. StyTr^2: Unbiased Image Style Transfer with Transformers (Yingying Deng et al., 2021)

Prerequisites

Prerequisites can be installed separately through the independent requirements.txt files in every implementation folder

pip install -r requirements.txt

Installation

This project is built with Python 3 for the latest Tensorflow version.

git clone https://github.com/DarshanDeshpande/research-paper-implementations.git
cd <name of implementation>

Contributing

Any and all code contributions are welcome. Please raise an issue if any implementation gives incorrect results or crashes unexpectedly during training or inference.

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Feel free to reach out for any issues or requests related to these implementations

Darshan Deshpande - Email | Twitter | LinkedIn