Generative Adversarial Networks (GANs) are presented as a transformative technology in machine learning, capable of generating new data and automating tasks that typically require large labeled datasets. The document highlights their applications in fields like image generation and data synthesis, along with potential risks such as adversarial attacks. GANs represent a significant step forward in enabling smarter and more autonomous AI systems without the need for extensive human data annotation.

1. Generative Adversarial
Networks (A.K.A GANs)
“Building something from nothing”
A (very) friendly introduction to
Csongor Barabasi
Artificial Intelligence Meetup Cluj Napoca

2. Who am I
Csongor Barabasi
Founder of Deep Learning School by Csongi
AI Student @ University of Birmingham

3. So … why GANs?
“Generative Adversarial Networks (GAN’s) are the
coolest thing since sliced bread. They are the coolest
thing in the last 10 years in ML”
Yann LeCun
Director of AI Research
Facebook

4. Machine Learning
“Machine Learning is the subfield of artificial intelligence
and it is based on algorithms that can learn from data
without relying on rule-based programming.”
McKinsey & Co.

5. Artificial
Intelligence
Machine
Learning Deep
Learning
Supervised Learning Unsupervised Learning

6. Supervised Learning
Size (sq.ft) Nr. bedrooms Price $
3100 7 60000
500 2 20000
2000 4 45000

7. Unsupervised Learning

8. Data
+ =
Keep feeding lots of data into your neural network until it yields a working model.
• Good for applications using existing, large, labelled datasets
• … but not suitable for niche applications, with small dataset
• Diagnosing a rare medical disease
Examples: FaceScrub (107,818), Skin Segmentation Dataset (245,057),
MNIST (60000), CIFAR-10/100 (60000)

9. What do we want to achieve
with AI?
1. Automated systems
• Not too smart
Artificial General Intelligence = handling novel tasks without explicit training
2. ML / DL
• The smartest we can get at the moment
3. GANs
• Less data, more intelligence

10. Convolutional Neural Networks

11. Convolutional Neural Networks

12. Inference vs Training

13. GAN Example

14. GAN Example
Go to the party
Get in or get
rejected
Feedback to make the ticket better

15. GAN
Random
Noise
Generator
“The Forger”
Synthesized
Data
Real
Data
Switch Discriminator
“The security”
Real
Fake
Feedback

16. Generative Model
• Tries to learn a representation of the data
• Then tries to create generate something new based on the
representation learned
• Composed of Deconvolution layers
Discriminative Model
• Learns a function that maps an input X to an output y
• Does classification
• GAN is an unsupervised technique trained in a supervised manner

17. When does the training end?
“It should happen until the generator exactly reproduces
the true data distribution and the discriminator is
guessing at random, unable to find a difference.”
Open AI

18. Cool thing #1
Data Synthesis
• Generate more data (usually for Deep Learning)

19. Cool thing #2
Learn translation between
datasets
• Low resolution to high resolution images

20. Cool thing #3
Adversarial attacks on DL
systems
• Scares the hell out of Deep Learning people

21. Interesting GAN applications

22. Image Generation

23. Text to Image

24. Neural Style Transfer

25. Interactive Image Generation
https://www.youtube.com/watch?v=5jfViPdYLic
https://www.youtube.com/watch?v=HOn8437TWPA

26. Sketch to Image

27. Anime Character Generation

28. 3D Object Generation

29. Vector Arithmetic on images

30. Wrapping up
1. GANs help us apply Deep Learning to new niches
• Humans do not have to create huge, labeled datasets. GANs can do it
• Smarter, more autonomous learning
• A toolkit for manipulating and merging datasets
2. GANs are disrupting deep learning
• Scary GAN based adversarial attacks
• Humans do not have to create huge, labeled datasets. GANs can do it
3. GANs hint at new AI capabilities
• More compelling human-AI collaboration

31. Thank you! :D
“What I cannot create, I cannot understand.”
Richard Feynman