contrastive-unpaired-translation - Efficient Image Translation Using Patchwise Contrastive Learning

Contrastive Unpaired Translation (CUT)

Contrastive Unpaired Translation (CUT) is a project that leverages machine learning techniques, specifically contrastive learning and adversarial learning, to achieve unpaired image-to-image translation. This allows the transformation of images from one domain to another without the need for paired examples. CUT is particularly efficient and effective, offering benefits over previous methods like CycleGAN by being faster and consuming less memory during training.

Key Features

Patchwise Contrastive Learning: Unlike traditional methods that may require complicated design choices such as hand-crafted loss functions or inverse networks, CUT simplifies the process by using patchwise contrastive learning. This approach helps in better aligning features across unpaired image domains.
Efficiency: The CUT model enhances training efficiency. It's designed to be faster and less resource-intensive, making it accessible for a broader range of applications, especially where computational resources may be limited.
Single Image Training: CUT can extend its capabilities to training with single images. This flexibility allows each domain to be just a single image, widening the scope of possible applications including artistic style transfer from a single reference image.

Example Applications

Unpaired Image-to-Image Translation: CUT can transform images from one domain to another without needing paired datasets. For instance, converting a photo of a cityscape into a painting or transforming a photo of a grumpy cat into various styles.
Single Image Unpaired Translation: An example is generating artistic styles from a single input image like the Etretat cliff, showing the potential for creative applications.

Getting Started

To begin using CUT, you will need a Linux or macOS operating system with Python 3 installed, along with either a CPU or NVIDIA GPU with CUDA support. Here’s a quick guide to set up your environment:

Clone the Repository: You can obtain the code from the GitHub repository using the command:
```
git clone https://github.com/taesungp/contrastive-unpaired-translation CUT
cd CUT
```
Install Dependencies: Depending on your preference, install PyTorch 1.1 and other necessary libraries using either pip with:
```
pip install -r requirements.txt
```
or Conda with:
```
conda env create -f environment.yml
```

Training and Testing with CUT

You can start training models using the grumpifycat dataset as a practical example. The process includes commands to train and test both CUT and its lighter variant FastCUT.
For those who prefer a more guided approach, the project offers launcher scripts simplifying the configuration and execution of training and testing commands.

Comparative Performance

CUT introduces a novel capability for more flexible and powerful distribution matching compared to methods like CycleGAN. The FastCUT variant trades some flexibility for speed and reduced memory usage, making it a viable alternative for certain tasks.

Additional Resources

For users interested in pre-trained models or evaluating the Fréchet Inception Distance (FID) of their results, the project includes scripts and guidance for running evaluations on various datasets, including Horse2Zebra and Cat2Dog transformations.

Conclusion

CUT is a significant advancement in the field of image translation, provide sophisticated capabilities while simplifying the workflow. It's a powerful tool for researchers and developers looking to explore unpaired image synthesis with greater ease and efficiency.

Citation and Acknowledgments

For academic usage, the project can be cited using its relevant academic paper. The project acknowledges contributions and inspirations from various existing resources and frameworks, which have supported its development and success.