1.
GAN Lab
Understanding Complex Deep Generative Models
using Interactive Visual Experimentation
Georgia Tech Google
Minsuk
Kahng
Nikhil
Thorat
Polo
Chau
Fernanda
Viégas
Martin
Wattenberg
Georgia Tech Google Google
PAIR | People + AI Research Initiative

2.
2
Deep learning visualization tools presented at VIS
Most tools are designed for experts

3.
Many non-experts want to learn ML
3
Chris Olah’s Blog Andrej Karpathy’s Demo

4.
Many non-experts want to learn ML
3
Chris Olah’s Blog Andrej Karpathy’s Demo
Can our VIS community help this population?

5.
5
TensorFlow Playground
TensorFlow Playground was a great success
http://playground.tensorflow.org

6.
Modern deep models are very complex
5

7.
Generative Adversarial Networks (GANs)
6
Hard to understand and train even for experts
“the most interesting idea in the last 10 years in ML”
- Yann LeCun
Face images generated by BEGAN [Berthelot et al., 2017]

8.
Why are GANs hard to understand?
Because a GAN uses two competing neural networks
7
Discriminator
spots fake
Police
spots fake bills
Generator
synthesizes outputs
Counterfeiter
makes fake bills

9.
7
How to explain this concept using visualization?
Discriminator
spots fake
Police
spots fake bills
Generator
synthesizes outputs
Counterfeiter
makes fake bills

10.
GAN Lab
First Interactive Tool for Learning GANs in Browser

11.
Key contributions of GAN Lab
• Novel visualization of GAN’s training process
• Interactive model training
• Browser-based implementation
8

12.
How did we visualize GANs?
9
Discriminator
spots fake
Police
spots fake bills
Generator
synthesizes outputs
Counterfeiter
makes fake bills

13.
2D data distribution, instead of high-dimensional images
10
What type of data to visualize?
Discriminator
(Police)
Generator
(Counterfeiter)

14.
2D data distribution, instead of high-dimensional images
1411
What type of data to visualize?
Discriminator
(Police)
Generator
(Counterfeiter)
1. Easier to visualize data distribution
2. Easier for learners to track dynamics
Why 2D data points?

15.
VER. 0.1
12
Real
(green)
Generated
(purple)

16.
How to visually explain the generator?
13
Generator
(Counterfeiter)

17.
How to visually explain the generator?
map an input point
into a new position
random
14
Generator
(Counterfeiter)

18.
How to visually explain the generator?
map an input point
into a new position
random
Manifold
?
14
Generator
(Counterfeiter)

19.
Mixture of
two Gaussians
15
How to visually explain the generator?

20.
How to visualize the discriminator?
16
Generator
(Counterfeiter)
Discriminator
(Police)

21.
2D heatmap, to represent its binary classification
17
How to visualize the discriminator?
Samples in this region are
likely real.
Samples are likely fake.

22.
VER. 0.5
18

23.
MODEL OVERVIEW GRAPH

24.
1. Building mental
models for GANs
How does it help?

25.
1. Building mental
models for GANs
2. Tracking data flow
How does it help?

26.
1. Building mental
models for GANs
2. Tracking data flow
3. Locating
hyperparameters
How does it help?

27.
GAN Lab broadens education access
24
Conventional Deep Learning Visualization
in JavaScript
in Python with GPU
Model Training
Visualization
$$$

28.
Everything done in browser, powered by TensorFlow.js
GAN Lab broadens education access
25
Accelerated by WebGL
in JavaScript
Visualization
also in JavaScript
Model Training

29.
GAN Lab is Live!
20K visitors, 100+ countries 1.9K Likes 800+ Retweets
Try at bit.ly/gan-lab

30.
Georgia Tech
Google PAIR
Minsuk Kahng
Nikhil Thorat
Polo Chau
Fernanda Viégas
Martin Wattenberg
Georgia Tech
Google PAIR
Google PAIR
minsuk.comLearning and Playing with
GANs in your browser!
PAIR People + AI Research|
GAN Lab
|
Try at bit.ly/gan-lab