BridgeGAN - Tensorflow implementation of Bridging the Gap between Label- and Reference-based Synthesis in Multi-attribute Image-to-Image Translation.

Last update: Jul 13, 2022

Related tags

Overview

Bridging the Gap between Label- and Reference based Synthesis(ICCV 2021)

Tensorflow implementation of Bridging the Gap between Label- and Reference-based Synthesis in Multi-attribute Image-to-Image Translation.

Overview architecture

Experiment Results

CelebA

Preparation

Prerequisites
- Tensorflow 1.15
- Python 2.x with matplotlib, numpy and scipy
Dataset
- CelebA
- Images should be placed in ./data/CelebA/img_align_celeba
- tfrecords file should be placed in ./data/CelebA/celeba_tfrecords

Quick Start

Exemplar commands are listed here for a quick start.

dataset

prepare dataset to product tfrecords file
```
python data_noise_in.py 
```

Training

To train with size of 128 X 128

python train_arch9.py --experiment_name "file_name" --gpu "gpu_num"

Testing

Example of test

python  test_arch9.py --experiment_name "file_name" --gpu "gpu_num"

Citation

If this work is useful for your research, please consider citing:

Owner

huangqiusheng

GitHub Repository

The codes I made while I practiced various TensorFlow examples

TensorFlow_Exercises The codes I made while I practiced various TensorFlow examples About the codes I didn't create these codes by myself, but re-crea

614 Dec 08, 2022

PyTorch code for Composing Partial Differential Equations with Physics-Aware Neural Networks

FInite volume Neural Network (FINN) This repository contains the PyTorch code for models, training, and testing, and Python code for data generation t

20 Dec 18, 2022

An open software package to develop BCI based brain and cognitive computing technology for recognizing user's intention using deep learning

272 Jan 08, 2023

Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch

PyVarInf PyVarInf provides facilities to easily train your PyTorch neural network models using variational inference. Bayesian Deep Learning with Vari

342 Dec 02, 2022

Multi-Task Learning as a Bargaining Game

Nash-MTL Official implementation of "Multi-Task Learning as a Bargaining Game". Setup environment conda create -n nashmtl python=3.9.7 conda activate

87 Dec 26, 2022

Pretrained SOTA Deep Learning models, callbacks and more for research and production with PyTorch Lightning and PyTorch

1.4k Jan 01, 2023

Multi-Stage Episodic Control for Strategic Exploration in Text Games

XTX: eXploit - Then - eXplore Requirements First clone this repo using git clone https://github.com/princeton-nlp/XTX.git Please create two conda envi

9 May 24, 2022

Python scripts for performing lane detection using the LSTR model in ONNX

ONNX LSTR Lane Detection Python scripts for performing lane detection using the Lane Shape Prediction with Transformers (LSTR) model in ONNX. Requirem

29 Aug 30, 2022

Toward Spatially Unbiased Generative Models (ICCV 2021)

Toward Spatially Unbiased Generative Models Implementation of Toward Spatially Unbiased Generative Models (ICCV 2021) Overview Recent image generation

88 Dec 01, 2022

Colour detection is necessary to recognize objects, it is also used as a tool in various image editing and drawing apps.

Colour Detection On Image Colour detection is the process of detecting the name of any color. Simple isn’t it? Well, for humans this is an extremely e

1 Jan 13, 2022

Analyzes your GitHub Profile and presents you with a report on how likely you are to become the next MLH Fellow!

Fellowship Prediction GitHub Profile Comparative Analysis Tool Built with BentoML Table of Contents: Features Disclaimer Technologies Used Contributin

51 Dec 29, 2022

Solution to the Weather4cast 2021 challenge

This code was used for the entry by the team "antfugue" for the Weather4cast 2021 Challenge. Below, you can find the instructions for generating predi

13 Jan 03, 2023

A baseline code for VSPW

A baseline code for VSPW Preparation Download VSPW dataset The VSPW dataset with extracted frames and masks is available here.

28 Aug 22, 2022

PyTorch implementation of DirectCLR from paper Understanding Dimensional Collapse in Contrastive Self-supervised Learning

DirectCLR DirectCLR is a simple contrastive learning model for visual representation learning. It does not require a trainable projector as SimCLR. It

49 Dec 21, 2022

Guiding evolutionary strategies by (inaccurate) differentiable robot simulators @ NeurIPS, 4th Robot Learning Workshop

Guiding Evolutionary Strategies by Differentiable Robot Simulators In recent years, Evolutionary Strategies were actively explored in robotic tasks fo

4 Dec 14, 2021

A modular domain adaptation library written in PyTorch.

225 Dec 29, 2022

Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.

3D Infomax improves GNNs for Molecular Property Prediction Video | Paper We pre-train GNNs to understand the geometry of molecules given only their 2D

95 Dec 30, 2022

EMNLP 2021 Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections

Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections Ruiqi Zhong, Kristy Lee*, Zheng Zhang*, Dan Klein EMN

42 Nov 03, 2022

Google Recaptcha solver.

byerecaptcha - Google Recaptcha solver. Model and some codes takes from embium's repository -Installation- pip install byerecaptcha -How to use- from

21 Dec 19, 2022

Code for generating the figures in the paper "Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?"

Code for running simulations for the paper "Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Lin

1 Nov 22, 2022