Cycle Consistent Adversarial Domain Adaptation (CyCADA)

Overview

Cycle Consistent Adversarial Domain Adaptation (CyCADA)

A pytorch implementation of CyCADA.

If you use this code in your research please consider citing

@inproceedings{Hoffman_cycada2017,
       authors = {Judy Hoffman and Eric Tzeng and Taesung Park and Jun-Yan Zhu,
             and Phillip Isola and Kate Saenko and Alexei A. Efros and Trevor Darrell},
       title = {CyCADA: Cycle Consistent Adversarial Domain Adaptation},
       booktitle = {International Conference on Machine Learning (ICML)},
       year = 2018
}

Setup

  • Check out the repo (recursively will also checkout the CyCADA fork of the CycleGAN repo).
    git clone --recursive https://github.com/jhoffman/cycada_release.git cycada
  • Install python requirements
    • pip install -r requirements.txt

Train image adaptation only (digits)

  • Image adaptation builds on the work on CycleGAN. The submodule in this repo is a fork which also includes the semantic consistency loss.
  • Pre-trained image results for digits may be downloaded here
  • Producing SVHN as MNIST
    • For an example of how to train image adaptation on SVHN->MNIST, see cyclegan/train_cycada.sh. From inside the cyclegan subfolder run train_cycada.sh.
    • The snapshots will be stored in cyclegan/cycada_svhn2mnist_noIdentity. Inside test_cycada.sh set the epoch value to the epoch you wish to use and then run the script to generate 50 transformed images (to preview quickly) or run test_cycada.sh all to generate the full ~73K SVHN images as MNIST digits.
    • Results are stored inside cyclegan/results/cycada_svhn2mnist_noIdentity/train_75/images.
    • Note we use a dataset of mnist_svhn and for this experiment run in the reverse direction (BtoA), so the source (SVHN) images translated to look like MNIST digits will be stored as [label]_[imageId]_fake_B.png. Hence when images from this directory will be loaded later we will only images which match that naming convention.

Train feature adaptation only (digits)

  • The main script for feature adaptation can be found inside scripts/train_adda.py
  • Modify the data directory you which stores all digit datasets (or where they will be downloaded)

Train feature adaptation following image adaptation

  • Use the feature space adapt code with the data and models from image adaptation
  • For example: to train for the SVHN to MNIST shift, set src = 'svhn2mnist' and tgt = 'mnist' inside scripts/train_adda.py
  • Either download the relevant images above or run image space adaptation code and extract transferred images

Train Feature Adaptation for Semantic Segmentation

CyCADA pixel+feat SVHN2MNIST test(ckevin4747)

Owner
Hyunwoo Ko
Student Researcher in Korea University.
Hyunwoo Ko
A universal memory dumper using Frida

Fridump Fridump (v0.1) is an open source memory dumping tool, primarily aimed to penetration testers and developers. Fridump is using the Frida framew

551 Jan 07, 2023
PanopticBEV - Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View Images

Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View Images This r

63 Dec 16, 2022
This repository includes the official project for the paper: TransMix: Attend to Mix for Vision Transformers.

TransMix: Attend to Mix for Vision Transformers This repository includes the official project for the paper: TransMix: Attend to Mix for Vision Transf

Jie-Neng Chen 130 Jan 01, 2023
Neural-fractal - Create Fractals Using Complex-Valued Neural Networks!

Neural Fractal Create Fractals Using Complex-Valued Neural Networks! Home Page Features Define Dynamical Systems Using Complex-Valued Neural Networks

Amirabbas Asadi 10 Dec 17, 2022
A python comtrade load library accelerated by go

Comtrade-GRPC Code for python used is mainly from dparrini/python-comtrade. Just patch the code in BinaryDatReader.parse for parsing a little more eff

Bo 1 Dec 27, 2021
Pytorch Implementation of Residual Vision Transformers(ResViT)

ResViT Official Pytorch Implementation of Residual Vision Transformers(ResViT) which is described in the following paper: Onat Dalmaz and Mahmut Yurt

ICON Lab 41 Dec 08, 2022
PyTorch implementation of MoCo v3 for self-supervised ResNet and ViT.

MoCo v3 for Self-supervised ResNet and ViT Introduction This is a PyTorch implementation of MoCo v3 for self-supervised ResNet and ViT. The original M

Facebook Research 887 Jan 08, 2023
Code for the paper "Controllable Video Captioning with an Exemplar Sentence"

SMCG Code for the paper "Controllable Video Captioning with an Exemplar Sentence" Introduction We investigate a novel and challenging task, namely con

10 Dec 04, 2022
The implementation for the SportsCap (IJCV 2021)

SportsCap: Monocular 3D Human Motion Capture and Fine-grained Understanding in Challenging Sports Videos ProjectPage | Paper | Video | Dataset (Part01

Chen Xin 79 Dec 16, 2022
HybridNets: End-to-End Perception Network

HybridNets: End2End Perception Network HybridNets Network Architecture. HybridNets: End-to-End Perception Network by Dat Vu, Bao Ngo, Hung Phan πŸ“§ FPT

Thanh Dat Vu 370 Dec 29, 2022
CryptoFrog - My First Strategy for freqtrade

cryptofrog-strategies CryptoFrog - My First Strategy for freqtrade NB: (2021-04-20) You'll need the latest freqtrade develop branch otherwise you migh

Robert Davey 137 Jan 01, 2023
This project uses ViT to perform image classification tasks on DATA set CIFAR10.

Vision-Transformer-Multiprocess-DistributedDataParallel-Apex Introduction This project uses ViT to perform image classification tasks on DATA set CIFA

Kaicheng Yang 3 Jun 03, 2022
Just playing with getting CLIP Guided Diffusion running locally, rather than having to use colab.

CLIP-Guided-Diffusion Just playing with getting CLIP Guided Diffusion running locally, rather than having to use colab. Original colab notebooks by Ka

Nerdy Rodent 336 Dec 09, 2022
Code to reproduce results from the paper "AmbientGAN: Generative models from lossy measurements"

AmbientGAN: Generative models from lossy measurements This repository provides code to reproduce results from the paper AmbientGAN: Generative models

Ashish Bora 87 Oct 19, 2022
python debugger and anti-vm that checks if you're in a virtual machine or if someones trying to debug your file

Anti-Debug was made by Love ❌ code βœ… πŸŽ‰ ・What it checks for ・ Kills tools that can be used to debug your file ・ Exits if ran in vm (supports different

Rdimo 31 Aug 09, 2022
Official implementation of Meta-StyleSpeech and StyleSpeech

Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation Dongchan Min, Dong Bok Lee, Eunho Yang, and Sung Ju Hwang This is an official code

min95 168 Dec 28, 2022
PyTorch code for our paper "Image Super-Resolution with Non-Local Sparse Attention" (CVPR2021).

Image Super-Resolution with Non-Local Sparse Attention This repository is for NLSN introduced in the following paper "Image Super-Resolution with Non-

143 Dec 28, 2022
E2EDNA2 - An automated pipeline for simulation of DNA aptamers complexed with small molecules and short peptides

E2EDNA2 - An automated pipeline for simulation of DNA aptamers complexed with small molecules and short peptides

11 Nov 08, 2022
WHENet: Real-time Fine-Grained Estimation for Wide Range Head Pose

WHENet: Real-time Fine-Grained Estimation for Wide Range Head Pose Yijun Zhou and James Gregson - BMVC2020 Abstract: We present an end-to-end head-pos

368 Dec 26, 2022
Winners of the Facebook Image Similarity Challenge

Winners of the Facebook Image Similarity Challenge

DrivenData 111 Jan 05, 2023