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

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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

Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX.

Python library containing BART query generation and BERT-based Siamese models for neural retrieval.

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Unofficial implement with paper SpeakerGAN: Speaker identification with conditional generative adversarial network

RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation (CIKM'17)

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Datasets and source code for our paper Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach

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