Official implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"

Related tags

Deep Learning DiscoGAN
Overview

DiscoGAN

Official PyTorch implementation of Learning to Discover Cross-Domain Relations with Generative Adversarial Networks.

Prerequisites

  • Python 2.7
  • PyTorch
  • Numpy/Scipy/Pandas
  • Progressbar
  • OpenCV

Training DiscoGAN

CelebA

Download CelebA dataset using

$ python ./datasets/download.py celebA

(Currently, the link for downloading CelebA dataset is not available).

To train gender conversion,

$ python ./discogan/image_translation.py --task_name='celebA' --style_A='Male'

To train hair color conversion

$ python ./discogan/image_translation.py --task_name='celebA' --style_A='Blond_Hair' --style_B='Black_Hair' --constraint='Male'

Handbags / Shoes

Download Edges2Handbags dataset using

$ python ./datasets/download.py edges2handbags

Download Edges2Shoes dataset using

$ python ./datasets/download.py edges2shoes

To train Edges2Handbags,

$ python ./discogan/image_translation.py --task_name='edges2handbags'

To train Edges2Shoes,

$ python ./discogan/image_translation.py --task_name='edges2shoes'

To train Handbags2Shoes,

$ python ./discogan/image_translation.py --task_name='Handbags2Shoes' --starting_rate=0.5

Facescrub

Download Facescrub dataset using

$ python ./datasets/download.py facescrub

To train gender conversion,

$ python ./discogan/image_translation.py --task_name='facescrub'

Car, Face

Download 3D car dataset used in Deep Visual Analogy-Making, and 3D face dataset into ./datasets folder and extract them.

To train Car2Car translation,

$ python ./discogan/angle_pairing.py --task_name='car2car'

To train Car2Face translation,

$ python ./discogan/angle_pairing.py --task_name='car2face'

Run script.sh in order to train a model using other datasaet, after uncommenting corresponding line.

Results

All example results show x_A, x_AB, x_ABA and x_B, x_BA, x_BAB

Example results of hair color conversion

Example results of gender conversion (CelebA)

Example results of Edges2Handbags

Example results of Handbags2Shoes

Example results of gender conversion (Facescrub)

Example results of Car2Face

Comments
  • gen loss formula

    gen loss formula

    in lines 251-252 you apply curriculum learning to compute the total gan loss. What's the motivation for that? Cannot find it in the paper.

    Besides, is there any authorship relation between this repo and https://github.com/carpedm20/DiscoGAN-pytorch ?

    opened by edgarriba 4
  • Fix some typos in image_translation.py

    Fix some typos in image_translation.py

    Fixed 3 typos:

    • image_translation.py
      • In the help information for image_save_interval and model_save_interval.
      • when calling function read_images, function name is typed as ges.
    • README.md
      • one typo for Facescrub dataset
    opened by TengdaHan 2
  • Fixed Syntax Error: invalid syntax

    Fixed Syntax Error: invalid syntax

    fixed: File "./datasets/download.py", line 146 def download_facescrub((data_dir, genders, names, urls, bboxes)): ______________________^ SyntaxError: invalid syntax Encountered in Google Colab Notebook

    opened by WinterSoldier13 0
  • Error in

    Error in "get_faces_3d" function . Error value : (need at least one array to stack)

    I was trying to train model for Face2Face conversion. But I am getting an error (need at least one array to stack). I have figured out how to fix it. We can fix it be adding the below mentioned line in DiscoGan/discogan/dataset.py . Function name "get_faces_3d".

    "image_paths = map(lambda x: os.path.join( face_3d_path, x ), image_files)"

    Add it before "return images_path" .
    @jazzsaxmafia

    opened by thevyom1 0
  • changed taskname in readme at line 54

    changed taskname in readme at line 54

    To train Handbags2Shoes model, the --task_name parameter is given wrong. It should be handbags2shoes instead Handbags2Shoes

    I hope this helps. Thanks.

    opened by kshru9 0
  • How to test model??

    How to test model??

    Hello Sir,

    I trained your code using edges2shoes. But I couldn't test.

    If you don't mind, please tell me test-code using trained-model and input-image.

    Thanks.

    opened by edwardcho 1
  • Does the number of datasets affect learning a lot?

    Does the number of datasets affect learning a lot?

    When the number of each data was 5000, learning did not work well. When the epoch increased a little, learning did not proceed. And it showed very bad results. I want to know what part of discoGAN is causing this result.

    opened by swyh 0
Owner
SK T-Brain
Artificial Intelligence
SK T-Brain
GitHub
The project is an official implementation of our CVPR2019 paper "Deep High-Resolution Representation Learning for Human Pose Estimation"

Deep High-Resolution Representation Learning for Human Pose Estimation (CVPR 2019) News [2020/07/05] A very nice blog from Towards Data Science introd

Leo Xiao 3.9k Jan 5, 2023
Official implementation of AAAI-21 paper "Label Confusion Learning to Enhance Text Classification Models"

Description: This is the official implementation of our AAAI-21 accepted paper Label Confusion Learning to Enhance Text Classification Models. The str

null 101 Nov 25, 2022
Official PyTorch implementation for paper Context Matters: Graph-based Self-supervised Representation Learning for Medical Images

Context Matters: Graph-based Self-supervised Representation Learning for Medical Images Official PyTorch implementation for paper Context Matters: Gra

null 49 Nov 23, 2022
The official implementation of NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation [ICLR-2021]. https://arxiv.org/pdf/2101.12378.pdf

NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation [ICLR-2021] Release Notes The offical PyTorch implementation of NeMo, p

Angtian Wang 76 Nov 23, 2022
StyleGAN2-ADA - Official PyTorch implementation

Abstract: Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge. We propose an adaptive discriminator augmentation mechanism that significantly stabilizes training in limited data regimes.

NVIDIA Research Projects 3.2k Dec 30, 2022
Official implementation of the ICLR 2021 paper

You Only Need Adversarial Supervision for Semantic Image Synthesis Official PyTorch implementation of the ICLR 2021 paper "You Only Need Adversarial S

Bosch Research 272 Dec 28, 2022
Official PyTorch implementation of Joint Object Detection and Multi-Object Tracking with Graph Neural Networks

This is the official PyTorch implementation of our paper: "Joint Object Detection and Multi-Object Tracking with Graph Neural Networks". Our project website and video demos are here.

Richard Wang 443 Dec 6, 2022
Official implementation of the paper Image Generators with Conditionally-Independent Pixel Synthesis https://arxiv.org/abs/2011.13775

CIPS -- Official Pytorch Implementation of the paper Image Generators with Conditionally-Independent Pixel Synthesis Requirements pip install -r requi

Multimodal Lab @ Samsung AI Center Moscow 201 Dec 21, 2022
Official pytorch implementation of paper "Image-to-image Translation via Hierarchical Style Disentanglement".

HiSD: Image-to-image Translation via Hierarchical Style Disentanglement Official pytorch implementation of paper "Image-to-image Translation

null 364 Dec 14, 2022
Official pytorch implementation of paper "Inception Convolution with Efficient Dilation Search" (CVPR 2021 Oral).

IC-Conv This repository is an official implementation of the paper Inception Convolution with Efficient Dilation Search. Getting Started Download Imag

Jie Liu 111 Dec 31, 2022
Official PyTorch Implementation of Unsupervised Learning of Scene Flow Estimation Fusing with Local Rigidity

UnRigidFlow This is the official PyTorch implementation of UnRigidFlow (IJCAI2019). Here are two sample results (~10MB gif for each) of our unsupervis

Liang Liu 28 Nov 16, 2022
Official implementation of our paper "LLA: Loss-aware Label Assignment for Dense Pedestrian Detection" in Pytorch.

LLA: Loss-aware Label Assignment for Dense Pedestrian Detection This project provides an implementation for "LLA: Loss-aware Label Assignment for Dens

null 35 Dec 6, 2022
Official implementation of Self-supervised Graph Attention Networks (SuperGAT), ICLR 2021.

SuperGAT Official implementation of Self-supervised Graph Attention Networks (SuperGAT). This model is presented at How to Find Your Friendly Neighbor

Dongkwan Kim 127 Dec 28, 2022
An official implementation of "SFNet: Learning Object-aware Semantic Correspondence" (CVPR 2019, TPAMI 2020) in PyTorch.

PyTorch implementation of SFNet This is the implementation of the paper "SFNet: Learning Object-aware Semantic Correspondence". For more information,

CV Lab @ Yonsei University 87 Dec 30, 2022
This project is the official implementation of our accepted ICLR 2021 paper BiPointNet: Binary Neural Network for Point Clouds.

BiPointNet: Binary Neural Network for Point Clouds Created by Haotong Qin, Zhongang Cai, Mingyuan Zhang, Yifu Ding, Haiyu Zhao, Shuai Yi, Xianglong Li

Haotong Qin 59 Dec 17, 2022
Official code implementation for "Personalized Federated Learning using Hypernetworks"

Personalized Federated Learning using Hypernetworks This is an official implementation of Personalized Federated Learning using Hypernetworks paper. [

Aviv Shamsian 121 Dec 25, 2022
StyleGAN2 - Official TensorFlow Implementation

StyleGAN2 - Official TensorFlow Implementation

NVIDIA Research Projects 10.1k Dec 28, 2022
Old Photo Restoration (Official PyTorch Implementation)

Bringing Old Photo Back to Life (CVPR 2020 oral)

Microsoft 11.3k Dec 30, 2022
Official implementation of "GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators" (NeurIPS 2020)

GS-WGAN This repository contains the implementation for GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators (NeurIPS

null 46 Nov 9, 2022