Generative Adversarial Network (GANs).

We can understand the GANs by simple mems

1. GENERATIVE ADVERSARIAL NETWORKS (GANS):

2. GANs - Generative Adversarial Networks Introduced by Ian Goodfellow and other researchers from the University of Montreal in 2014 WHAT ARE GANs? It is a neural network that belongs to the set of generative AI They are capable of generating new content that has never been generated before GANS fall under the category of Unsupervised Learning It can be applied to various domains such as images, music, speech, and writing The architecture consists of two neural networks: the Generator and the Discriminator

3. THE EVOLUTION OF GANs It was a major breakthrough in the field of Deep Learning and Artificial Neural Networks. Since its publication, it has remained one of the most popular and relevant topics within the field of Artificial Intelligence and Deep Learning. It caused a significant advancement as until then other technologies were not achieving satisfactory results. GANs came as a revolution, being the first to produce high- quality results on most of the datasets they were trained on .

4. CONVOLUTIONAL NEURAL NETWORK ( CNN ) To understand GANs first we must have little understanding of Convolutional Neural Networks. CNNs are trained to classify images with respect to their labels if an image is fed to a CNN, it analyzes the image pixel by pixel and is passed through nodes present in CNN's hidden layers and as an output, it tells what the image is about or what it sees in the image . For example: If a CNN is trained to classify dogs and cats and an image is fed to this CNN, it can tell whether there is a dog or a cat in that image. Therefore it can also be called as a classification algorithm

5. HOW GANs WORK Generator generate new instances of data Discriminator evaluates the authenticity of the generated data GENERATOR DISCRIMINATOR Images of the Training set

6. STEPS OF A GAN The Generator considers random values to generate an image – input. The generated image is sent to the Discriminator along with a stream of images taken from the real dataset (to use as reference). The Discriminator returns the probabilities of the generated image being real. The result is a number between (fake) and 1 (authentic).

7. STEPS OF A GAN

8. HOW GANs WORK The discriminator network is a standard convolutional network (binary classifier that labels images as real or false). The generator is the opposite: while the standard convolutional classifier takes the image and downsamples it to return a probability, the generator takes a random noise vector and upsamples it to generate an image that will be analyzed by the discriminator. Both the discriminator and the generator are trying to optimize a different and opposite fitness function (loss function). During training, the generator and the discriminator will change their behavior. They will be one against the other because of the losses. This is why we say that the two networks “compete” with each other.

9. HOW GANs WORK GENERATOR NETWORK DISCRIMINATOR NETWORK

10. TRAINING DISCRIMINATOR The discriminator network is trained to distinguish between real and generated data, playing an adversarial role with the generator . It uses a loss function to measure the difference between real and fake data, guiding the training of the entire GAN system.

11. TRAINING GENERATOR Training the generator network to create realistic data from noise.

12. TRAINING GENERATOR

13. HOW GANs WORK

14. HOW GANs WORK

15. TYPES OF GANs Some examples: DCGANs (Deep Convolutional) WGANs (Wasserstein) SRGANs (Super resolution) Pix2Pix (Image-to-image) CycleGAN (Cycle Generative) StackGAN (Stacked GAN) ProGAN (Progressive Growing) StyleGAN (Style-Based) VQGAN (Vector Quantized) And other lesser known (but very interesting) extensions: SGAN InfoGAN SAGAN AC- GAN GauGAN GFP- GAN

16. GANs - APPLICATIONS Paired image-to- image translation pix2pix

17. GANs - APPLICATIONS Super Resolution upsampling a low- resolution image into a higher resolution r

18. GANs - APPLICATIONS Text-to- Image Generation (text2image) Generating Synthetic Images from textual description

19. GANs - APPLICATIONS Facial rejuvenation and aging (and other facial operations such as adding a beard, changing facial expression, etc.)

20. GANs - APPLICATIONS Generate faces of people that don't exist

21. GANs - APPLICATIONS Fill missing parts of the image (inpainting)

22. GANs - APPLICATIONS Generating images of new human poses

23. GANs - APPLICATIONS Restoration of old images and noise removal

24. Conclusion and Future Directions Progress - Continued advancements in GAN technology . Research - Exploring new applications and improvements . Integration - Integrating GAN into various fields and industries.