StarGAN v2-Tensorflow - Simple Tensorflow implementation of StarGAN v2

Last update: Jul 02, 2022

Related tags

Deep Learning StarGAN_v2-Tensorflow

Overview

Official Tensorflow implementation Open ! -> Clova AI

StarGAN v2 — Un-official TensorFlow Implementation [Paper] [Pytorch]

: Diverse Image Synthesis for Multiple Domains

Overview

Architecture

Generator	Mapping Network	Style Encoder & Discriminator

Usage

├── dataset
   └── YOUR_DATASET_NAME
       ├── train
           ├── domain1 (domain folder)
               ├── xxx.jpg (domain1 image)
               ├── yyy.png
               ├── ...
           ├── domain2
               ├── aaa.jpg (domain2 image)
               ├── bbb.png
               ├── ...
           ├── domain3
           ├── ...
       ├── test
           ├── zzz.jpg (any content image)
           ├── www.png
           ├── ...

Train

python main.py --dataset celebA-HQ_gender --phase train --batch_size 2 --gpu_num 4

Test

python main.py --dataset celebA-HQ_gender --phase test

Refer test

python main.py --dataset celebA-HQ_gender --phase refer_test --refer_img_path refer_img.jpg

Our results

Author

Junho Kim

Owner

Junho Kim

Research Scientist

GitHub Repository

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StarGAN v2-Tensorflow - Simple Tensorflow implementation of StarGAN v2

Related tags

Overview

Official Tensorflow implementation Open ! -> Clova AI

StarGAN v2 — Un-official TensorFlow Implementation [Paper] [Pytorch]

: Diverse Image Synthesis for Multiple Domains

Overview

Architecture

Usage

Train

Test

Refer test

Our results

Author

Owner

Junho Kim

(NeurIPS 2020) Wasserstein Distances for Stereo Disparity Estimation

LogAvgExp - Pytorch Implementation of LogAvgExp

This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations,

In this project we combine techniques from neural voice cloning and musical instrument synthesis to achieve good results from as little as 16 seconds of target data.

Massively parallel Monte Carlo diffusion MR simulator written in Python.

The code written during my Bachelor Thesis "Classification of Human Whole-Body Motion using Hidden Markov Models".

Machine learning algorithms for many-body quantum systems

Gradient Inversion with Generative Image Prior

Implémentation en pyhton de l'article Depixelizing pixel art de Johannes Kopf et Dani Lischinski

[ICML 2021] A fast algorithm for fitting robust decision trees.

CONditionals for Ordinal Regression and classification in tensorflow

StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation

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Deep motion transfer

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