EuroSciPy 2019: https://pretalx.com/euroscipy-2019/talk/Q79NND/ GANs are the new hottest topic in the ML arena; however, they present a challenge for the researchers and the engineers alike. Their design, and most importantly, the code implementation has been causing headaches to the ML practitioners, especially when moving to production. The workshop aims at providing a complete understanding of both the theory and the practical know-how to code and deploy this family of models in production. By the end of it, the attendees should be able to apply the concepts learned to other models without any issues. We will be showcasing all the shiny new APIs introduced by TensorFlow 2.0 by showing how to build a GAN from scratch and how to "productionize" it by leveraging the AshPy Python package that allows to easily design, prototype, train and export Machine Learning models defined in TensorFlow 2.0. The workshop is composed of: - Theoretical introduction - GANs from Scratch in TensorFlow 2.0 - High-performance input data pipeline with TensorFlow Datasets - Introduction to the AshPy API - Implementing, training, and visualizing DCGAN using AshPy - Serving TF2 Models with Google Cloud Functions The materials of the workshop will be openly provided via GitHub (https://github.com/zurutech/gans-from-theory-to-production).

1. GAN - Theory and Applications
Michele De Simoni @mr_ubik
Federico Di Mattia @_iLeW_
Paolo Galeone @paolo_galeone
Emanuele Ghelfi @manughelfi
https://bit.ly/2Y1nqay
August 30, 2019
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3. Overview
1. Introduction
2. Models definition
3. GANs Training
4. Types of GANs
5. GANs Applications
3

4. Introduction

5. “
Generative Adversarial Networks is the most
interesting idea in the last ten years in
machine learning.
Yann LeCun, Director, Facebook AI
”
4

6. Generative Adversarial Networks
Two components, the generator and the discriminator:
• The generator G needs to capture the data distribution.
• The discriminator D estimates the probability that a sample
comes from the training data rather than from G.
Figure 1: Credits: Silva
5

7. Generative Adversarial Networks
GANs game:
min
G
max
D
VGAN(D, G) = E
x∼pdata(x)
[log D(x)]+ E
z∼pz (z)
[log(1−D(G(z)))]
6

8. Generative Adversarial Networks
GANs game:
min
G
max
D
VGAN(D, G) = E
x∼pdata(x)
[log D(x)]
real samples
+ E
z∼pz (z)
[log(1−D(G(z)))]
6

9. Generative Adversarial Networks
GANs game:
min
G
max
D
VGAN(D, G) = E
x∼pdata(x)
[log D(x)]
real samples
+ E
z∼pz (z)
[log(1 − D(G(z)))]
generated samples
6

10. GANs - Discriminator
• Discriminator needs to:
• Correctly classify real data:
max
D
E
x∼pdata(x)
[log D(x)] D(x) → 1
• Correctly classify wrong data:
max
D
E
z∼pz (z)
[log(1 − D(G(z)))] D(G(z)) → 0
• The discriminator is an adaptive loss function.
7

12. GANs - Generator
• Generator needs to fool the discriminator:
• Generate samples similar to the real ones:
min
G
E
z∼pz (z)
[log(1 − D(G(z)))] D(G(z)) → 1
9

13. GANs - Generator
• Generator needs to fool the discriminator:
• Generate samples similar to the real ones:
min
G
E
z∼pz (z)
[log(1 − D(G(z)))] D(G(z)) → 1
• Non saturating objective (Goodfellow et al., 2014):
min
G
E
z∼pz (z)
[− log(D(G(z)))]
9

14. GANs - Generator Objectives
• Minimax: log(1 − D(G(z)))
0 0.5 1
−6
−4
−2
0
2
4
D(G(z))
JG
Minimax
10

15. GANs - Generator Objectives
• Minimax: log(1 − D(G(z)))
• Non-saturating: − log(D(G(z)))
0 0.5 1
−6
−4
−2
0
2
4
D(G(z))
JG
Minimax
Non-saturating
10

16. Models definition

17. GANs - Models definition
• Different architectures for different data types.
• Tuple of numbers? Fully Connected Neural Networks
11

18. GANs - Models definition
• Different architectures for different data types.
• Text or sequences? Recurrent Neural Networks
11

19. GANs - Models definition
• Different architectures for different data types.
• Images? Convolutional Neural Networks
1
latent
1
fc
32768
256
*conv1
32
128
*conv2
64
64
*conv3
128
K
*conv4
128
3 128
conv5
Latent Vector Conv/Deconv Fully Connected Batch Norm Relu
11

20. GANs Training

21. GANs - Training
• D and G are competing against each other.
• Alternating execution of training steps.
• Use minibatch stochastic gradient descent/ascent.
12

22. GANs - Training - Discriminator
How to train the discriminator?
Repeat from 1 to k:
1. Sample minibatch of m noise samples z(1), . . . , z(m) from pz(z)
13

23. GANs - Training - Discriminator
How to train the discriminator?
Repeat from 1 to k:
1. Sample minibatch of m noise samples z(1), . . . , z(m) from pz(z)
2. Sample minibatch of m examples x(1), . . . , x(m) from pdata(x)
13

24. GANs - Training - Discriminator
How to train the discriminator?
Repeat from 1 to k:
1. Sample minibatch of m noise samples z(1), . . . , z(m) from pz(z)
2. Sample minibatch of m examples x(1), . . . , x(m) from pdata(x)
3. Update D:
J =
1
m
m
i=1
log D(x(i)
) + log(1 − D(G(z(i)
)))
D performance
θd = θd + λ θd
J
13

25. GANs - Training - Generator
How to train the generator?
Update executed only once after D updates:
1. Sample minibatch of m noise samples z(1), . . . , z(m) from pz(z)
14

26. GANs - Training - Generator
How to train the generator?
Update executed only once after D updates:
1. Sample minibatch of m noise samples z(1), . . . , z(m) from pz(z)
2. Update G:
J =
1
m
m
i=1
log(D(G(z(i)
)))
G performance
θg = θg + λ θgJ
14

27. GANs - Training - Considerations
• Optimizers: Adam, Momentum, RMSProp.
• Arbitrary number of steps or epochs.
• Training is completed when D is completely fooled by G.
• Goal: reach a Nash Equilibrium where the best D can do is
random guessing.
15

28. Types of GANs

29. Types of GANs
Two big families:
• Unconditional GANs (just described).
• Conditional GANs (Mirza and Osindero, 2014).
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30. Conditional GANs
• Both G and D are conditioned on some extra information y.
• In practice: perform conditioning by feeding y into D and G.
Figure 2: From Mirza and Osindero (2014)
17

31. Conditional GANs
The GANs game becomes:
min
G
max
D
E
x∼pdata(x|y)
[log D(x, y)] + E
z∼pz (z)
[log(1 − D(G(z|y), y))]
Notice: the same representation of the condition has to be
presented to both network.
18

32. GANs Applications

33. Unconditional - Face Generation - Karras et al. (2017)
19

34. Conditional - Domain Translation - Isola et al. (2016)
20

35. Conditional - Semantic Image Synthesis - Park et al. (2018)
21

36. Conditional - Image Super Resolution - Ledig et al. (2016)
22

37. Real-world GANs
• Semi-Supervised Learning (Salimans et al., 2016)
• Image Generation (almost all GAN papers)
• Image Captioning
• Anomalies Detection (Zenati et al., 2018)
• Program Synthesis (Ganin et al., 2018)
• Genomics and Proteomics (Killoran et al., 2017) (De Cao and
Kipf, 2018)
• Personalized GANufactoring (Hwang et al., 2018)
• Planning

38. References
[De Cao and Kipf 2018] De Cao, Nicola ; Kipf, Thomas: MolGAN:
An Implicit Generative Model for Small Molecular Graphs.
(2018). – (2018)
[Ganin et al. 2018] Ganin, Yaroslav ; Kulkarni, Tejas ; Babuschkin,
Igor ; Eslami, S. M. A. ; Vinyals, Oriol: Synthesizing Programs for
Images Using Reinforced Adversarial Learning. (2018). – (2018)
[Goodfellow et al. 2014] Goodfellow, Ian J. ; Pouget-Abadie,
Jean ; Mirza, Mehdi ; Xu, Bing ; Warde-Farley, David ; Ozair,
Sherjil ; Courville, Aaron ; Bengio, Yoshua: Generative
Adversarial Networks. (2014). – (2014)

39. [Hwang et al. 2018] Hwang, Jyh-Jing ; Azernikov, Sergei ; Efros,
Alexei A. ; Yu, Stella X.: Learning Beyond Human Expertise with
Generative Models for Dental Restorations. (2018). – (2018)
[Isola et al. 2016] Isola, Phillip ; Zhu, Jun-Yan ; Zhou, Tinghui ;
Efros, Alexei A.: Image-to-Image Translation with Conditional
Adversarial Networks. (2016). – (2016)
[Karras et al. 2017] Karras, Tero ; Aila, Timo ; Laine, Samuli ;
Lehtinen, Jaakko: Progressive Growing of GANs for Improved
Quality, Stability, and Variation. (2017). – (2017)
[Killoran et al. 2017] Killoran, Nathan ; Lee, Leo J. ; Delong,
Andrew ; Duvenaud, David ; Frey, Brendan J.: Generating and
Designing DNA with Deep Generative Models. (2017). – (2017)

40. [Ledig et al. 2016] Ledig, Christian ; Theis, Lucas ; Huszar,
Ferenc ; Caballero, Jose ; Cunningham, Andrew ; Acosta,
Alejandro ; Aitken, Andrew ; Tejani, Alykhan ; Totz, Johannes ;
Wang, Zehan ; Shi, Wenzhe: Photo-Realistic Single Image
Super-Resolution Using a Generative Adversarial Network.
(2016). – (2016)
[Mirza and Osindero 2014] Mirza, Mehdi ; Osindero, Simon:
Conditional Generative Adversarial Nets. (2014). – (2014)
[Park et al. 2018] Park, Taesung ; Liu, Ming-Yu ; Wang,
Ting-Chun ; Zhu, Jun-Yan: Semantic Image Synthesis with
Spatially-Adaptive Normalization. (2018). – (2018)
[Salimans et al. 2016] Salimans, Tim ; Goodfellow, Ian ;
Zaremba, Wojciech ; Cheung, Vicki ; Radford, Alec ; Chen, Xi:
Improved Techniques for Training GANs. (2016). – (2016)

41. [Silva ] Silva, Thalles: An Intuitive Introduction to
Generative Adversarial Networks (GANs)
[Zenati et al. 2018] Zenati, Houssam ; Foo, Chuan S. ; Lecouat,
Bruno ; Manek, Gaurav ; Chandrasekhar, Vijay R.: Efficient
GAN-Based Anomaly Detection. (2018). – (2018)