https://github.com/openai/infogan
Code for reproducing key results in the paper "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets"
https://github.com/openai/infogan
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Last synced: 12 months ago
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Code for reproducing key results in the paper "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets"
- Host: GitHub
- URL: https://github.com/openai/infogan
- Owner: openai
- Created: 2016-06-02T20:13:24.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2021-03-25T08:17:44.000Z (about 5 years ago)
- Last Synced: 2025-04-09T08:06:02.329Z (about 1 year ago)
- Topics: paper
- Language: Python
- Homepage: https://arxiv.org/abs/1606.03657
- Size: 18.6 KB
- Stars: 1,060
- Watchers: 208
- Forks: 305
- Open Issues: 24
-
Metadata Files:
- Readme: README.md
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README
**Status:** Archive (code is provided as-is, no updates expected)
# InfoGAN
Code for reproducing key results in the paper [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://arxiv.org/abs/1606.03657) by Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel.
## Dependencies
This project currently requires the dev version of TensorFlow available on Github: https://github.com/tensorflow/tensorflow. As of the release, the latest commit is [79174a](https://github.com/tensorflow/tensorflow/commit/79174afa30046ecdc437b531812f2cb41a32695e).
In addition, please `pip install` the following packages:
- `prettytensor`
- `progressbar`
- `python-dateutil`
## Running in Docker
```bash
$ git clone git@github.com:openai/InfoGAN.git
$ docker run -v $(pwd)/InfoGAN:/InfoGAN -w /InfoGAN -it -p 8888:8888 gcr.io/tensorflow/tensorflow:r0.9rc0-devel
root@X:/InfoGAN# pip install -r requirements.txt
root@X:/InfoGAN# python launchers/run_mnist_exp.py
```
## Running Experiment
We provide the source code to run the MNIST example:
```bash
PYTHONPATH='.' python launchers/run_mnist_exp.py
```
You can launch TensorBoard to view the generated images:
```bash
tensorboard --logdir logs/mnist
```