TransGAN: Two Transformers Can Make One Strong GAN

Last update: Jan 07, 2023

Overview

TransGAN: Two Transformers Can Make One Strong GAN

Code used for TransGAN: Two Transformers Can Make One Strong GAN.

Main Pipeline

Visual Results

prepare fid statistic file

mkdir fid_stat

Download the pre-calculated statistics (Google Drive) to ./fid_stat.

Environment

pip install -r requirements.txt

Notice: Pytorch version has to be <=1.3.0 !

Training

Coming soon

Testing

Firstly download the checkpoint from (Google Drive) to ./pretrained_weight

# cifar-10
sh exps/cifar10_test.sh

# stl-10
sh exps/stl10_test.sh

Acknowledgement

FID code and CIFAR-10 statistics file from https://github.com/bioinf-jku/TTUR (official). Codebase from AutoGAN

Owner

VITA

Visual Informatics Group @ University of Texas at Austin

GitHub Repository

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TransGAN: Two Transformers Can Make One Strong GAN

Related tags

Overview

TransGAN: Two Transformers Can Make One Strong GAN

Main Pipeline

Visual Results

prepare fid statistic file

Environment

Training

Testing

Acknowledgement

Owner

VITA

This repository contains several image-to-image translation models, whcih were tested for RGB to NIR image generation. The models are Pix2Pix, Pix2PixHD, CycleGAN and PointWise.

Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation

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