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

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Deep LearningBridgeGAN
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
huangqiusheng
GitHub Repository
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