Slides for my talk on Data Science Milan

1. Generative modeling
beyond creepy GIFs in Twitter
Alexandr Honchar

2. • AI architect @ Mawi Solutions
• AI consultant @ CIS, EU
• Masters in maths candidate @
University of Verona
• Blogger @ Medium

3. Why GANs today?

4. https://twitter.com/goodfellow_ian/status/969776035649675265

5. https://medium
.com/@LeonFe
dden/learning-
london-dating-
profiles-
8f13403af1d9

6. https://blog.openai.com/glow/

7. Random artists from Twitter, Telegram

8. https://njustesen.
com/2018/11/13/a
n-exploring-of-
biggans-
truncation-
threshold-to-
create-artistic-
horrific-images-
of-masks/

9. https://twitter.
com/_joelsim
on/status/106
824357076584
8581

10. https://www.chri
sties.com/featur
es/A-
collaboration-
between-two-
artists-one-
human-one-a-
machine-9332-
1.aspx

11. But I don’t need all that!
I am serious guy who needs data science to earn money, not Twitter likes

12. Generative vs discriminative ML
A bit of scary mathematics (no)

13. Space of cats and dogs
A very deep neural network
already created some nice
embedding space for us from
initial pixel space

14. Discriminative modeling:
A function f(x, w) telling us where
new x belongs to from {cat, dog}

15. Generative modeling:
A modeled distribution P(x|y),
where x - our data point and y
belongs to {cat, dog}
So, we can or create new cat from
P(x|y=“cat”), or check if some x_i
belongs to P(x_i|y=“dog”) using
well known maths

16. Generative modeling:
A modeled distribution P(x|y),
where x - our data point and y
belongs to {cat, dog}
So, we can or create new cat from
P(x|y=“cat”), or check if some x_i
belongs to P(x_i|y=“dog”) using
well known maths

17. Natural manifold hypothesis:
real-world high dimensional data
(such as images) lie on low-
dimensional manifolds embedded
in the high-dimensional space
Short tails, a little fur
Long tails, a lot of fur

18. Not only images!

19. Video game level generation
Text generation
Drug molecule generation, Insilico
Music generation, Google Magenta

20. P(X)
What value has generation of objects?

22. The RAPIDD Ebola
forecasting challenge: Model
description and synthetic
data generation
GAN-based Synthetic Medical Image Augmentation for
increased CNN Performance in Liver Lesion
Classification
IoT data generation, aidrome

23. Short tails, a little fur
Long tails, a lot of fur

24. TL-GAN: transparent latent-space GAN

25. Medical Image Synthesis for Data
Augmentation and Anonymization using
Generative Adversarial Networks
Numerai / Erasure for financial data
prediction

26. ~P(X)
What are all other objects?

28. https://skymind.ai/wiki/deep-autoencoder
https://www.researchgate.net/figure/Gene
rative-Adversarial-Network-
GAN_fig1_317061929

29. P(X) -> P(Y)
What value has turning some objects to another ones?

30. https://docs.neptune.ml/get-started/style-transfer/
X Y
P(X) ->
P(Y)

31. CycleGAN

32. https://cari-gan.github.io

33. Everybody Dance Now

34. Short tails, a little fur
Long tails, a lot of fur

35. • Voice from the phone <-> Voice from HQ mic, SEGAN
• ECG from hospital <-> ECG from a wristband, Mawi
• Official text <-> Funny text, MaskGAN
• Dressed person <-> Naked person, DeepFakes
• CCTV camera <-> HD camera

36. 8x8 to 128x128 Super Resolution with Adversarial Autoencoders

37. Takeaways

38. • Generative modeling >= Discriminative modeling
• “I do not understand things which I cannot create”
• We need to share data to third parties
• We need to manipulate our data with simple factors
• We need anomaly detectors and reject options
• We need to adapt our data for some conditions

39. FACEBOOK: @rachnogstyle
MEDIUM / TWITTER: @alexrachnog
alex@mawi.band