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

Last update: Jul 13, 2022

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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

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

Related tags

Overview

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

Experiment Results

Preparation

Quick Start

dataset

Training

Testing

Citation

Owner

huangqiusheng

Code for the upcoming CVPR 2021 paper

Two types of Recommender System : Content-based Recommender System and Colaborating filtering based recommender system

A basic implementation of Layer-wise Relevance Propagation (LRP) in PyTorch.

CoRe: Contrastive Recurrent State-Space Models

Flower classification model that classifies flowers in 10 classes made using transfer learning (~85% accuracy).

Deep learning-based approach to discovering Granger causality networks in multivariate time series

Unified Pre-training for Self-Supervised Learning and Supervised Learning for ASR

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Official code of the paper "Expanding Low-Density Latent Regions for Open-Set Object Detection" (CVPR 2022)

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Megaverse is a new 3D simulation platform for reinforcement learning and embodied AI research