What Are GANs? | Generative Adversarial Networks Tutorial | Deep Learning Tutorial | Simplilearn

2. What’s in it for you? • What are Generative Adversarial Networks • Generator • Discriminator • How GANs work? • Types of GANs • Applications of GANs

3. What are Generative Adversarial Networks? Generative Adversarial Networks consist of two models that compete with each other to analyze, capture and copy the variations within a dataset Real Discriminator Fake Generator

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5. Generator The Generator in GAN learns to create fake data by incorporating feedback from the discriminator Generator network Fake Image Random Input

6. Generator Training Discriminator Loss Real Images Sample Discriminator Generator Loss Generator Sample Random Input Backpropagation

7. Discriminator The Discriminator in GAN is a classifier that identifies real data from the fake data created by the Generator Real Images Discriminator Network Fake Image

8. Discriminator Training Backpropagation Discriminator Discriminator Loss Real Images Sample Generator Loss Generator Sample Random Input

9. How GANs Work? GANs consist of 2 networks, a Generator G(x) and a Discriminator D(x) Real Images Discriminator Network Predicted Labels Real / Fake notes Generator Network Noise vector Fakes images

10. How GANs Work? The Generator learn the distribution of data and is trained to increase the probability of the Discriminator network to make mistakes Real Images Discriminator Network Predicted Labels Real / Fake notes Generator Network Noise vector Fakes images

11. How GANs Work? The Discriminator estimates the probability that the sample it got is from the training data and not from the Generator Real Images Discriminator Network Predicted Labels Real / Fake notes Generator Network Noise vector Fakes images

12. How GANs Work? The mathematical formula for working on GANs can be represented as: V(D, G) = Ex~Pdata(x) [logD(x)] + Ez~p(z) [log(1 – D(G(z))] Where, G = Generator D = Discriminator Pdata(x) = distribution of real data p(z) = distribution of generator x = sample from Pdata(x) z = sample from P(z)D(x) = Discriminator networkG(z) = Generator network

13. How GANs Work? Steps for training GAN • Define the problem • Choose the architecture of GAN • Train Discriminator on real data • Generate fake data for Generator • Train Discriminator on fake data • Train Generator with the output of Discriminator

14. Types of GANs Deep Convolutional GANs (DCGANs) Vanilla GANs Simplest type of GAN where the Generator and Discriminator are simple multi-layer perceptrons DCGANs comprise of ConvNets and are more stable and generate higher quality images

15. Types of GANs Conditional GANs (CGANs) Super Resolution GAN (SRGAN) SRGANs generate a photorealistic high-resolution image when given a low-resolution image CGANs use extra label information to generate better results

16. Applications of GANs 1 Generating Cartoon Characters Using DCGANs, you can create faces of anime and Pokemon characters

17. Applications of GANs 2 Generating Human Faces GANs can be trained on the images of humans to generate realistic faces

18. Applications of GANs 3 Text to image translation GANs can build realistic images from textual descriptions of simple objects like birds Bird with a black head, yellow body and a short beak

19. Applications of GANs 4 3-D Object Generation GANs can generate 3-D models using 2-D pictures of objects from multiple perspectives

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