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https://github.com/poloclub/ganlab
GAN Lab: An Interactive, Visual Experimentation Tool for Generative Adversarial Networks
https://github.com/poloclub/ganlab
Last synced: 7 days ago
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GAN Lab: An Interactive, Visual Experimentation Tool for Generative Adversarial Networks
- Host: GitHub
- URL: https://github.com/poloclub/ganlab
- Owner: poloclub
- License: apache-2.0
- Created: 2018-03-28T03:23:07.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-12-08T18:05:00.000Z (almost 2 years ago)
- Last Synced: 2024-08-02T05:16:18.465Z (3 months ago)
- Language: JavaScript
- Homepage: https://poloclub.github.io/ganlab/
- Size: 5.23 MB
- Stars: 1,381
- Watchers: 55
- Forks: 380
- Open Issues: 27
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-list - GAN Lab - An Interactive, Visual Experimentation Tool for Generative Adversarial Networks. (Computer Vision / Image / Video Generation)
README
# GAN Lab: An Interactive, Visual Experimentation Tool for Generative Adversarial Networks
By
[Minsuk Kahng](http://minsuk.com),
[Nikhil Thorat](https://twitter.com/nsthorat),
[Polo Chau](https://www.cc.gatech.edu/~dchau/),
[Fernanda Viégas](http://fernandaviegas.com/), and
[Martin Wattenberg](http://www.bewitched.com/)
## Overview
GAN Lab is a novel interactive visualization tool for anyone to learn and experiment with Generative Adversarial Networks (GANs), a popular class of complex deep learning models. With GAN Lab, you can interactively train GAN models for 2D data distributions and visualize their inner-workings, similar to [TensorFlow Playground](http://playground.tensorflow.org/).
GAN Lab uses [TensorFlow.js](https://js.tensorflow.org/), an in-browser GPU-accelerated deep learning library. Everything, from model training to visualization, is implemented with JavaScript. Users only need a web browser like Chrome to run GAN Lab. Our implementation approach significantly broadens people's access to interactive tools for deep learning.
## Working Demo
Click the following link:
[https://poloclub.github.io/ganlab/](https://poloclub.github.io/ganlab/)
It runs on most modern web browsers. We suggest you use Google Chrome.
## Development
This section describes how you can develop GAN Lab.
### Install Dependencies
Run the following commands:
```bash
$ git clone https://github.com/poloclub/ganlab.git
$ cd ganlab
$ yarn prep
```
It's unlikely, but you may need to install some basic JavaScript-related dependencies (e.g., yarn).
### Running Your Demo
Run the following command:
```bash
$ ./scripts/watch-demo
>> Waiting for initial compile...
>> 3462522 bytes written to demo/bundle.js (2.17 seconds) at 00:00:00
>> Starting up http-server, serving ./
>> Available on:
>> http://127.0.0.1:8080
>> Hit CTRL-C to stop the server
```
Then visit `http://localhost:8080/demo/`.
The `watch-demo` script monitors for changes of typescript code (e.g., `demo/ganlab.ts`)
and compiles the code for you.
## Credit
GAN Lab was created by
[Minsuk Kahng](http://minsuk.com),
[Nikhil Thorat](https://twitter.com/nsthorat),
[Polo Chau](https://www.cc.gatech.edu/~dchau/),
[Fernanda Viégas](http://www.fernandaviegas.com/), and
[Martin Wattenberg](http://www.bewitched.com/),
which was the result of a research collaboration between Georgia Tech and Google Brain/[PAIR](https://ai.google/research/teams/brain/pair).
We also thank Shan Carter and Daniel Smilkov,
[Google Big Picture team](https://research.google.com/bigpicture/) and
[Google People + AI Research (PAIR)](https://ai.google/research/teams/brain/pair), and
[Georgia Tech Visualization Lab](http://vis.gatech.edu/)
for their feedback.
For more information, check out
[our research paper](http://minsuk.com/research/papers/kahng-ganlab-vast2018.pdf):
[Minsuk Kahng](http://minsuk.com),
[Nikhil Thorat](https://twitter.com/nsthorat),
[Polo Chau](https://www.cc.gatech.edu/~dchau/),
[Fernanda Viégas](http://www.fernandaviegas.com/), and
[Martin Wattenberg](http://www.bewitched.com/).
"GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation."
*IEEE Transactions on Visualization and Computer Graphics, 25(1) ([VAST 2018](http://ieeevis.org/year/2018/welcome))*, Jan. 2019.