IEGAN — Official PyTorch Implementation Independent Encoder for Deep Hierarchical Unsupervised Image-to-Image Translation

Related tags

Deep LearningIEGAN
Overview

IEGAN — Official PyTorch Implementation

Independent Encoder for Deep Hierarchical Unsupervised Image-to-Image Translation

Independent Encoder for Deep Hierarchical Unsupervised Image-to-Image Translation

Abstract The main challenges of image-to-image translation are to make the translated image realistic and retain as much information from the source domain as possible. To address this issue, we propose a novel architecture, termed as IEGAN, which removes the encoder of each network and introduces an encoder that is independent of other networks. Compared with previous models, it embodies three advantages of our model: Firstly, it is more directly and comprehensively to grasp image information since the encoder no longer receives loss from generator and discriminator. Secondly, the independent encoder allows each network to focus more on its own goal which makes the translated image more realistic. Thirdly, the reduction in the number of encoders performs more unified image representation. However, when the independent encoder applies two down-sampling blocks, it's hard to extract semantic information. To tackle this problem, we propose deep and shallow information space containing characteristic and semantic information, which can guide the model to translate high-quality images under the task with significant shape or texture change. We compare IEGAN with other previous models, and conduct researches on semantic information consistency and component ablation at the same time. These experiments show the superiority and effectiveness of our architecture.

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
           └── ...

Prerequisites

  • Python 3.6.13
  • Pytorch 1.2.0 and torchvision 0.4.0 (https://pytorch.org/)
  • linear_attention_transformer
  • CUDA 10.0.130, CuDNN 7.6, and CentOS 7.8.

GPU memory occupied size

  • In the actual situation of using Tesla P100, IEGAN will occupy 9709MiB

Train

> CUDA_VISIBLE_DEVICES=X python3 main.py --dataset=cat2dog
  • X choose the GPU to use

Restoring from the previous checkpoint

> CUDA_VISIBLE_DEVICES=X python3 main.py --dataset cat2dog --resume True

Test

> python3 main.py --dataset cat2dog --phase test

Metric

> CUDA_VISIBLE_DEVICES=X python3 fid_kid.py testA fakeA --mmd-var 
  • You can use gpu, set X to the index of gpu, such as CUDA_VISIBLE_DEVICES=0

Network

Comparison

Acknowledgments

Our code is inspired by NICE-GAN-pytorch.

PyTorch implementations of Top-N recommendation, collaborative filtering recommenders.

PyTorch implementations of Top-N recommendation, collaborative filtering recommenders.

Yoonki Jeong 129 Dec 22, 2022
FcaNet: Frequency Channel Attention Networks

FcaNet: Frequency Channel Attention Networks PyTorch implementation of the paper "FcaNet: Frequency Channel Attention Networks". Simplest usage Models

327 Dec 27, 2022
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models (published in ICLR2018)

Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models Pouya Samangouei*, Maya Kabkab*, Rama Chellappa [*: authors co

Maya Kabkab 212 Dec 07, 2022
Code Repo for the ACL21 paper "Common Sense Beyond English: Evaluating and Improving Multilingual LMs for Commonsense Reasoning"

Common Sense Beyond English: Evaluating and Improving Multilingual LMs for Commonsense Reasoning This is the Github repository of our paper, "Common S

INK Lab @ USC 19 Nov 30, 2022
BrainGNN - A deep learning model for data-driven discovery of functional connectivity

A deep learning model for data-driven discovery of functional connectivity https://doi.org/10.3390/a14030075 Usman Mahmood, Zengin Fu, Vince D. Calhou

Usman Mahmood 3 Aug 28, 2022
Byte-based multilingual transformer TTS for low-resource/few-shot language adaptation.

One model to speak them all 🌎 Audio Language Text ▷ Chinese 人人生而自由,在尊严和权利上一律平等。 ▷ English All human beings are born free and equal in dignity and rig

Mutian He 60 Nov 14, 2022
Code for MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks

MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks This is the code for the paper: MentorNet: Learning Data-Driven Curriculum fo

Google 302 Dec 23, 2022
Research using Cirq!

ReCirq Research using Cirq! This project contains modules for running quantum computing applications and experiments through Cirq and Quantum Engine.

quantumlib 230 Dec 29, 2022
A new video text spotting framework with Transformer

TransVTSpotter: End-to-end Video Text Spotter with Transformer Introduction A Multilingual, Open World Video Text Dataset and End-to-end Video Text Sp

weijiawu 67 Jan 03, 2023
The LaTeX and Python code for generating the paper, experiments' results and visualizations reported in each paper is available (whenever possible) in the paper's directory

This repository contains the software implementation of most algorithms used or developed in my research. The LaTeX and Python code for generating the

João Fonseca 3 Jan 03, 2023
Pytorch implementation of Masked Auto-Encoder

Masked Auto-Encoder (MAE) Pytorch implementation of Masked Auto-Encoder: Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick

Jiyuan 22 Dec 13, 2022
Fader Networks: Manipulating Images by Sliding Attributes - NIPS 2017

FaderNetworks PyTorch implementation of Fader Networks (NIPS 2017). Fader Networks can generate different realistic versions of images by modifying at

Facebook Research 753 Dec 23, 2022
Multi-layer convolutional LSTM with Pytorch

Convolution_LSTM_pytorch Thanks for your attention. I haven't got time to maintain this repo for a long time. I recommend this repo which provides an

Zijie Zhuang 733 Dec 30, 2022
Solving SMPL/MANO parameters from keypoint coordinates.

Minimal-IK A simple and naive inverse kinematics solver for MANO hand model, SMPL body model, and SMPL-H body+hand model. Briefly, given joint coordin

Yuxiao Zhou 305 Dec 30, 2022
✅ How Robust are Fact Checking Systems on Colloquial Claims?. In NAACL-HLT, 2021.

How Robust are Fact Checking Systems on Colloquial Claims? Official PyTorch implementation of our NAACL paper: Byeongchang Kim*, Hyunwoo Kim*, Seokhee

Byeongchang Kim 19 Mar 15, 2022
Model Zoo for AI Model Efficiency Toolkit

We provide a collection of popular neural network models and compare their floating point and quantized performance.

Qualcomm Innovation Center 137 Jan 03, 2023
A python library for implementing a recommender system

python-recsys A python library for implementing a recommender system. Installation Dependencies python-recsys is build on top of Divisi2, with csc-pys

Oscar Celma 1.5k Dec 17, 2022
Flexible Networks for Learning Physical Dynamics of Deformable Objects (2021)

Flexible Networks for Learning Physical Dynamics of Deformable Objects (2021) By Jinhyung Park, Dohae Lee, In-Kwon Lee from Yonsei University (Seoul,

Jinhyung Park 0 Jan 09, 2022
Revisting Open World Object Detection

Revisting Open World Object Detection Installation See INSTALL.md. Dataset Our n

58 Dec 23, 2022
Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)

Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)

Yihui He 1k Jan 03, 2023