Torch implementation of various types of GAN (e.g. DCGAN, ALI, Context-encoder, DiscoGAN, CycleGAN, EBGAN, LSGAN)

Related tags

Deep Learning deep-learning torch generative-adversarial-network dcgan gans adversarial context-encoder ali
Overview

image

gans-collection.torch

Torch implementation of various types of GANs (e.g. DCGAN, ALI, Context-encoder, DiscoGAN, CycleGAN, EBGAN). Note that EBGAN and BEGAN implementation is still not stable yet. I am working on this.

image

Contents

Prerequisites

  • Torch7
  • python2.7
  • cuda
  • other torch packages (display, hdf5, image ...)

Usage

  1. download training data:
python download.py --datasets <dataset>
(e.g) python run.py --datasets celebA

---------------------------------------
The training data folder should look like : 
<train_data_root>
                |--classA
                        |--image1A
                        |--image2B ...
                |--classB
                        |--image1B
                        |--image2B ...
---------------------------------------
  1. run GANs training:
    Note that you need to change parameter options in "script/opts.lua" for each GANs.
python run.py --type <gan_type>
(e.g) python run.py --type dcgan

Display GUI : How to see generated images in real-time?

step by step instruction:

1. set server-related options(ip, port, etc.) in "script.opts.lua"
2. run server (python server.py --type <gan_type>)
3. open web browser, and connect. (https://<server_ip>:<server_port>)

you will see like this: image

Results

training Final

Acknowledgement

Author

MinchulShin, @nashory
Will keep updating other types of GANs.
Any insane bug reports or questions are welcome. (min.stellastra[at]gmail.com) :-)

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Comments
  • differences between cyclegan and discogan

    differences between cyclegan and discogan

    (https://www.quora.com/What-is-the-difference-between-CycleGAN-and-DiscoGAN-They-both-seem-to-be-the-same-thing)

    • CycleGAN has a single cycle-consistency loss. Also, when comparing F(G(y)) with y, it seems that the two papers use different forms of distance measures (MSE, hinge-loss).
    • CycleGan has an addition hyperparameter to adjust the contribution of reconstruction/cycle-consistency loss in the overall loss function.
    • the generator structures used in DiscoGan and CycleGan are somewhat different.
    opened by nashory 3
Owner
Minchul Shin
Deep Learning, Computer Vision | Research Scientist at kakaobrain (2021-present) | ex-SWE at NAVER (2017-2021)
Minchul Shin
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