EigenGAN Tensorflow, EigenGAN: Layer-Wise Eigen-Learning for GANs

Overview
Gender Bangs Body Side Pose (Yaw)
Lighting Smile Face Shape Lipstick Color
Painting Style Pose (Yaw) Pose (Pitch) Zoom & Rotate
Flush & Eye Color Mouth Shape Hair Color Hue (Orange-Blue)

More Unsupervisedly Learned Dimensions


EigenGAN

TensorFlow implementation of EigenGAN: Layer-Wise Eigen-Learning for GANs      

Usage

  • Environment

    • Python 3.6

    • TensorFlow 1.15

    • OpenCV, scikit-image, tqdm, oyaml

    • we recommend Anaconda or Miniconda, then you can create the environment with commands below

      conda create -n EigenGAN python=3.6
      
      source activate EigenGAN
      
      conda install opencv scikit-image tqdm tensorflow-gpu=1.15
      
      conda install -c conda-forge oyaml
    • NOTICE: if you create a new conda environment, remember to activate it before any other command

      source activate EigenGAN
  • Data Preparation

    • CelebA-unaligned (10.2GB, higher quality than the aligned data)

      • download the dataset

      • unzip and process the data

        7z x ./data/img_celeba/img_celeba.7z/img_celeba.7z.001 -o./data/img_celeba/
        
        unzip ./data/img_celeba/annotations.zip -d ./data/img_celeba/
        
        python ./scripts/align.py
    • Anime

      • download the dataset

        mkdir -p ./data/anime
        
        rsync --verbose --recursive rsync://78.46.86.149:873/biggan/portraits/ ./data/anime/original_imgs
      • process the data

        python ./scripts/remove_black_edge.py
  • Run (support multi-GPU)

    • training on CelebA

      CUDA_VISIBLE_DEVICES=0,1 \
      python train.py \
      --img_dir ./data/img_celeba/aligned/align_size(572,572)_move(0.250,0.000)_face_factor(0.450)_jpg/data \
      --experiment_name CelebA
    • training on Anime

      CUDA_VISIBLE_DEVICES=0,1 \
      python train.py \
      --img_dir ./data/anime/remove_black_edge_imgs \
      --experiment_name Anime
    • testing

      CUDA_VISIBLE_DEVICES=0 \
      python test_traversal_all_dims.py \
      --experiment_name CelebA
    • loss visualization

      CUDA_VISIBLE_DEVICES='' \
      tensorboard \
      --logdir ./output/CelebA/summaries \
      --port 6006
  • Using Trained Weights

    • trained weights (move to ./output/*.zip)

    • unzip the file (CelebA.zip for example)

      unzip ./output/CelebA.zip -d ./output/
    • testing (see above)

Citation

If you find EigenGAN useful in your research works, please consider citing:

@article{he2021eigengan,
  title={EigenGAN: Layer-Wise Eigen-Learning for GANs},
  author={He, Zhenliang and Kan, Meina and Shan, Shiguang},
  journal={arXiv:2104.12476},
  year={2021}
}
Owner
Zhenliang He
Zhenliang He
Analysis of Antarctica sequencing samples contaminated with SARS-CoV-2

Analysis of SARS-CoV-2 reads in sequencing of 2018-2019 Antarctica samples in PRJNA692319 The samples analyzed here are described in this preprint, wh

Jesse Bloom 4 Feb 09, 2022
DANA paper supplementary materials

DANA Supplements This repository stores the data, results, and R scripts to generate these reuslts and figures for the corresponding paper Depth Norma

0 Dec 17, 2021
Language Models Can See: Plugging Visual Controls in Text Generation

Language Models Can See: Plugging Visual Controls in Text Generation Authors: Yixuan Su, Tian Lan, Yahui Liu, Fangyu Liu, Dani Yogatama, Yan Wang, Lin

Yixuan Su 195 Dec 22, 2022
Code for "LASR: Learning Articulated Shape Reconstruction from a Monocular Video". CVPR 2021.

LASR Installation Build with conda conda env create -f lasr.yml conda activate lasr # install softras cd third_party/softras; python setup.py install;

Google 157 Dec 26, 2022
FANet - Real-time Semantic Segmentation with Fast Attention

FANet Real-time Semantic Segmentation with Fast Attention Ping Hu, Federico Perazzi, Fabian Caba Heilbron, Oliver Wang, Zhe Lin, Kate Saenko , Stan Sc

Ping Hu 42 Nov 30, 2022
Specificity-preserving RGB-D Saliency Detection

Specificity-preserving RGB-D Saliency Detection Authors: Tao Zhou, Huazhu Fu, Geng Chen, Yi Zhou, Deng-Ping Fan, and Ling Shao. 1. Preface This reposi

Tao Zhou 35 Jan 08, 2023
A Moonraker plug-in for real-time compensation of frame thermal expansion

Frame Expansion Compensation A Moonraker plug-in for real-time compensation of frame thermal expansion. Installation Credit to protoloft, from whom I

58 Jan 02, 2023
TensorFlow Implementation of "Show, Attend and Tell"

Show, Attend and Tell Update (December 2, 2016) TensorFlow implementation of Show, Attend and Tell: Neural Image Caption Generation with Visual Attent

Yunjey Choi 902 Nov 29, 2022
🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱

Monitor deep learning model training and hardware usage from mobile. 🔥 Features Monitor running experiments from mobile phone (or laptop) Monitor har

labml.ai 1.2k Dec 25, 2022
Code for Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding

🍐 quince Code for Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding 🍐 Installation $ git clone

Andrew Jesson 19 Jun 23, 2022
Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency[ECCV 2020]

Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency(ECCV 2020) This is an official python implementati

304 Jan 03, 2023
Deploying PyTorch Model to Production with FastAPI in CUDA-supported Docker

Deploying PyTorch Model to Production with FastAPI in CUDA-supported Docker A example FastAPI PyTorch Model deploy with nvidia/cuda base docker. Model

Ming 68 Jan 04, 2023
A python module for scientific analysis of 3D objects based on VTK and Numpy

A lightweight and powerful python module for scientific analysis and visualization of 3d objects.

Marco Musy 1.5k Jan 06, 2023
PyTorch code for the ICCV'21 paper: "Always Be Dreaming: A New Approach for Class-Incremental Learning"

Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning PyTorch code for the ICCV 2021 paper: Always Be Dreaming: A New Approach f

49 Dec 21, 2022
frida工具的缝合怪

fridaUiTools fridaUiTools是一个界面化整理脚本的工具。新人的练手作品。参考项目ZenTracer,觉得既然可以界面化,那么应该可以把功能做的更加完善一些。跨平台支持:win、mac、linux 功能缝合怪。把一些常用的frida的hook脚本简单统一输出方式后,整合进来。并且

diveking 997 Jan 09, 2023
A PaddlePaddle implementation of STGCN with a few modifications in the model architecture in order to forecast traffic jam.

About This repository contains the code of a PaddlePaddle implementation of STGCN based on the paper Spatio-Temporal Graph Convolutional Networks: A D

Tianjian Li 1 Jan 11, 2022
Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification

Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification Suncheng Xiang Shanghai Jiao Tong University Over

SunchengXiang 68 Dec 13, 2022
CC-GENERATOR - A python script for generating CC

CC-GENERATOR A python script for generating CC NOTE: This tool is for Educationa

Lêkzï 6 Oct 14, 2022
Generative Models as a Data Source for Multiview Representation Learning

GenRep Project Page | Paper Generative Models as a Data Source for Multiview Representation Learning Ali Jahanian, Xavier Puig, Yonglong Tian, Phillip

Ali 81 Dec 03, 2022
Goal of the project : Detecting Temporal Boundaries in Sign Language videos

MVA RecVis course final project : Goal of the project : Detecting Temporal Boundaries in Sign Language videos. Sign language automatic indexing is an

Loubna Ben Allal 6 Dec 21, 2022