Code for reproducing key results in the paper "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets"

Last update: Dec 19, 2022

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Deep Learning paper

Overview

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 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.

In addition, please pip install the following packages:

prettytensor
progressbar
python-dateutil

Running in Docker

$ git clone [email protected]:openai/InfoGAN.git
$ docker run -v $(pwd)/InfoGAN:/InfoGAN -w /InfoGAN -it -p 8888:8888 gcr.io/tensorflow/tensorflow:r0.9rc0-devel
[email protected]:/InfoGAN# pip install -r requirements.txt
[email protected]:/InfoGAN# python launchers/run_mnist_exp.py

Running Experiment

We provide the source code to run the MNIST example:

PYTHONPATH='.' python launchers/run_mnist_exp.py

You can launch TensorBoard to view the generated images:

tensorboard --logdir logs/mnist

Code for reproducing key results in the paper "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets"

Related tags

Overview

InfoGAN

Dependencies

Running in Docker

Running Experiment

Owner

OpenAI

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