Attention-guided gan for synthesizing IR images

Last update: Oct 25, 2021

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Overview

SI-AGAN

Attention-guided gan for synthesizing IR images

This repository contains the Tensorflow code for "Pedestrian Gender Recognition by Style Transfer of Visible-Light Image to Infrared-Light Image Based on an Attention-Guided Generative Adversarial Network". This code is based on the TensorFlow implementation of AGGAN provide by AlamiMejjati.

I am working on updating to Tensorflow 2.0, and when it is updated, I will add a link to the new repository.

Dataset

Training

revise .json file in configs folder
make .csv file using create_cyclegan_dataaset.py
start training

python main.py --to_train=1 --log_dir=./output/##/## --config_filename=./configs/##.json

Also, you can check the tensorbaord

tensorboard --port=#### --logdir=./output/##/##/#timestamp#

Testing

python main.py --to_train=0 --log_dir=./output/##/## --config_filename=./configs/##.json --checkpoint_dir=./output/##/##/#timestamp#

Owner

GitHub Repository

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Attention-guided gan for synthesizing IR images

Related tags

Overview

SI-AGAN

Dataset

Training

Testing

Owner

Procedural 3D data generation pipeline for architecture

An off-line judger supporting distributed problem repositories

Riemannian Convex Potential Maps

Learning Compatible Embeddings, ICCV 2021

This is the code for Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning

공공장소에서 눈만 돌리면 CCTV가 보인다는 말이 과언이 아닐 정도로 CCTV가 우리 생활에 깊숙이 자리 잡았습니다.

A Python package for faster, safer, and simpler ML processes

Capsule endoscopy detection DACON challenge

GNPy: Optical Route Planning and DWDM Network Optimization

Evaluation suite for large-scale language models.

Implementation based on Paper - Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

Mortgage-loan-prediction - Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.

Differential Privacy for Heterogeneous Federated Learning : Utility & Privacy tradeoffs

Introducing neural networks to predict stock prices

DeepFaceLab fork which provides IPython Notebook to use DFL with Google Colab

ReferFormer - Official Implementation of ReferFormer

Official implementation of the RAVE model: a Realtime Audio Variational autoEncoder

STYLER: Style Factor Modeling with Rapidity and Robustness via Speech Decomposition for Expressive and Controllable Neural Text to Speech

Attention-guided gan for synthesizing IR images

Related tags

Overview

SI-AGAN

Dataset

Training

Testing

Owner

Procedural 3D data generation pipeline for architecture

An off-line judger supporting distributed problem repositories

Riemannian Convex Potential Maps

Learning Compatible Embeddings, ICCV 2021

This is the code for Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning

공공장소에서 눈만 돌리면 CCTV가 보인다는 말이 과언이 아닐 정도로 CCTV가 우리 생활에 깊숙이 자리 잡았습니다.

A Python package for faster, safer, and simpler ML processes

Capsule endoscopy detection DACON challenge

GNPy: Optical Route Planning and DWDM Network Optimization

Evaluation suite for large-scale language models.

Implementation based on Paper - Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

Mortgage-loan-prediction - Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing.

Differential Privacy for Heterogeneous Federated Learning : Utility & Privacy tradeoffs

Introducing neural networks to predict stock prices

DeepFaceLab fork which provides IPython Notebook to use DFL with Google Colab

ReferFormer - Official Implementation of ReferFormer

Official implementation of the RAVE model: a Realtime Audio Variational autoEncoder

STYLER: Style Factor Modeling with Rapidity and Robustness via Speech Decomposition for Expressive and Controllable Neural Text to Speech

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.