UNIT-Tensorflow

Simple Tensorflow implementation of "Unsupervised Image to Image Translation Networks" (NIPS 2017 Spotlight)

Requirements

• Tensorflow 1.4
• Python 3.6

Usage

├── dataset
   └── YOUR_DATASET_NAME
       ├── trainA
           ├── xxx.jpg (name, format doesn't matter)
           ├── yyy.png
           └── ...
       ├── trainB
           ├── zzz.jpg
           ├── www.png
           └── ...
       ├── testA
           ├── aaa.jpg 
           ├── bbb.png
           └── ...
       └── testB
           ├── ccc.jpg 
           ├── ddd.png
           └── ...

> python main.py --phase train --dataset cat2tiger

• See main.py for other arguments
• If you want to multi_gpu_version, then use main_multi_gpu.py (batch_size = The batch_size per gpu)
• If you want to faster_UNIT, then use DatasetAPI (code is more simple !)

Issue

Too much Slow !!!

• The slower reason is that it stores checkpoints
• If you want to speed up, do not save checkpoints per iteration

Arichitecture

Framework

Model

Training Objective

Result

Success

Fail

Related works

• CycleGAN-Tensorflow
• DiscoGAN-Tensorflow
• MUNIT-Tensorflow
• StarGAN-Tensorflow
• DRIT-Tensorflow

Reference

• UNIT-Pytorch
• Multi-GPU-Tensorflow
• DatasetAPI-Tensorflow

Author

Junho Kim