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[ICCV 2019] AutoGAN: Neural Architecture Search for Generative Adversarial Networks
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README.md

AutoGAN: Neural Architecture Search for Generative Adversarial Networks

The implementation of AutoGAN: Neural Architecture Search for Generative Adversarial Networks.

Introduction

We've desinged a novel neural architecture search framework for generative adversarial networks (GANs), dubbed AutoGAN. Experiments validate the effectiveness of AutoGAN on the task of unconditional image generation. Specifically, our discovered architectures achieve highly competitive performance on unconditional image generation task of CIFAR-10, which obtains a record FID score of 12.42, a competitive Inception score of 8.55.

RNN controller:

ctrl

Search space:

ss

Discovered network architecture:

cifar_arch1

Performance

Unconditional image generation on CIFAR-10.

cifar10_res

Unconditional image generation on STL-10.

stl10_res

Set-up

install libraries:

python >= 3.6

pip install -r requirements.txt

prepare fid statistic file

mkdir fid_stat

Download the pre-calculated statistics (Google Drive) to ./fid_stat.

How to train & test

train

sh exps/autogan_cifar10_a.sh

test

Run the following script:

python test.py \
--dataset cifar10 \
--img_size 32 \
--bottom_width 4 \
--model autogan_cifar10_a \
--latent_dim 128 \
--gf_dim 256 \
--g_spectral_norm False \
--load_path /path/to/*.pth \
--exp_name test_autogan_cifar10_a

Pre-trained models are provided (Google Drive).

Citation

If you find this work is useful to your research, please cite our paper:

@InProceedings{Gong_2019_ICCV,
author = {Gong, Xinyu and Chang, Shiyu and Jiang, Yifan and Wang, Zhangyang},
title = {AutoGAN: Neural Architecture Search for Generative Adversarial Networks},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2019}
}

Acknowledgement

  1. Inception Score code from OpenAI's Improved GAN (official).
  2. FID code and CIFAR-10 statistics file from https://github.com/bioinf-jku/TTUR (official).
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