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Simple Tensorflow implementation of "Large Scale GAN Training for High Fidelity Natural Image Synthesis" (BigGAN)
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README.md

BigGAN-Tensorflow

Simple Tensorflow implementation of "Large Scale GAN Training for High Fidelity Natural Image Synthesis" (BigGAN)

main

Issue

  • The paper used orthogonal initialization, but I used random normal initialization. The reason is, when using the orthogonal initialization, it did not train properly.
  • I have applied a hierarchical latent space, but not a class embeddedding.

Usage

dataset

  • mnist and cifar10 are used inside keras
  • For your dataset, put images like this:
├── dataset
   └── YOUR_DATASET_NAME
       ├── xxx.jpg (name, format doesn't matter)
       ├── yyy.png
       └── ...

train

  • python main.py --phase train --dataset celebA-HQ --gan_type hinge

test

  • python main.py --phase test --dataset celebA-HQ --gan_type hinge

Architecture

128x128

256x256

512x512

Author

Junho Kim

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