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GANs are Mathematically Represented by :
𝒎𝒊𝒏~𝑮, 𝒎𝒂𝒙~𝑫 𝑽 𝑫, 𝑮 = 𝑬𝒙~𝑷𝒅𝒂𝒕𝒂 𝒙
𝐥𝐨𝐠𝐃 𝐱 + 𝐄 𝐳~𝐩 𝐳
[𝐥𝐨𝐠(𝟏 − 𝐃 𝐆 𝐳 ]
Minimizing
Generator
Maximizing
Discriminator
Expectation value
over the probability
distribution of real
sample
Log of discriminator
output on real data
Expectation value
over the probability
distribution of random
noise
Log of compliment of
Discriminator output of
Generator from Random
Noise (z).
This makes sure that generator produces such a output that is difficult for
discriminator to catch](https://image.slidesharecdn.com/genrativeaiaiml-230603130118-ed7dc2fb/85/Exploring-Generative-AI-with-GAN-Models-11-320.jpg)
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“This data is from real sample” - Discriminator
Generator
Generated
Sample
Real Sample
Discriminator
𝒎𝒊𝒏~𝑮, 𝒎𝒂𝒙~𝑫 𝑽 𝑫, 𝑮 = 𝑬𝒙~𝑷𝒅𝒂𝒕𝒂 𝒙
𝐥𝐨𝐠𝐃 𝐱 + 𝐄 𝐳~𝐩 𝐳
[𝐥𝐨𝐠(𝟏 − 𝐃 𝐆 𝐳 ]
Dominant Discriminator’s Loss function makes the generator’s learning difficult!](https://image.slidesharecdn.com/genrativeaiaiml-230603130118-ed7dc2fb/85/Exploring-Generative-AI-with-GAN-Models-12-320.jpg)
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“No, I generated this data” - Generator
Generated
Sample
Generator
Discriminator
𝒎𝒊𝒏~𝑮, 𝒎𝒂𝒙~𝑫 𝑽 𝑫, 𝑮 = 𝑬𝒙~𝑷𝒅𝒂𝒕𝒂 𝒙
𝐥𝐨𝐠𝐃 𝐱 + 𝐄 𝐳~𝐩 𝐳
[𝐥𝐨𝐠(𝟏 − 𝐃 𝐆 𝐳 ]
Equilibrium balance where generator fools the discriminator](https://image.slidesharecdn.com/genrativeaiaiml-230603130118-ed7dc2fb/85/Exploring-Generative-AI-with-GAN-Models-13-320.jpg)
![1
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GANs are Mathematically Represented by :
𝒎𝒊𝒏~𝑮, 𝒎𝒂𝒙~𝑫 𝑽 𝑫, 𝑮 = 𝑬𝒙~𝑷𝒅𝒂𝒕𝒂 𝒙
𝐥𝐨𝐠𝐃 𝐱 + 𝐄 𝐳~𝐩 𝐳
[𝐥𝐨𝐠(𝟏 − 𝐃 𝐆 𝐳 ]
Minimizing
Generator
Maximizing
Discriminator
Expectation value
over the probability
distribution of real
sample
Log of discriminator
output on real data
Expectation value
over the probability
distribution of random
noise
Log of compliment of
Discriminator output of
Generator from Random
Noise (z).
This makes sure that generator produces such a output that is difficult for
discriminator to catch](https://image.slidesharecdn.com/genrativeaiaiml-230603130118-ed7dc2fb/75/Exploring-Generative-AI-with-GAN-Models-11-2048.jpg)
![1
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“This data is from real sample” - Discriminator
Generator
Generated
Sample
Real Sample
Discriminator
𝒎𝒊𝒏~𝑮, 𝒎𝒂𝒙~𝑫 𝑽 𝑫, 𝑮 = 𝑬𝒙~𝑷𝒅𝒂𝒕𝒂 𝒙
𝐥𝐨𝐠𝐃 𝐱 + 𝐄 𝐳~𝐩 𝐳
[𝐥𝐨𝐠(𝟏 − 𝐃 𝐆 𝐳 ]
Dominant Discriminator’s Loss function makes the generator’s learning difficult!](https://image.slidesharecdn.com/genrativeaiaiml-230603130118-ed7dc2fb/75/Exploring-Generative-AI-with-GAN-Models-12-2048.jpg)
![1
2
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“No, I generated this data” - Generator
Generated
Sample
Generator
Discriminator
𝒎𝒊𝒏~𝑮, 𝒎𝒂𝒙~𝑫 𝑽 𝑫, 𝑮 = 𝑬𝒙~𝑷𝒅𝒂𝒕𝒂 𝒙
𝐥𝐨𝐠𝐃 𝐱 + 𝐄 𝐳~𝐩 𝐳
[𝐥𝐨𝐠(𝟏 − 𝐃 𝐆 𝐳 ]
Equilibrium balance where generator fools the discriminator](https://image.slidesharecdn.com/genrativeaiaiml-230603130118-ed7dc2fb/75/Exploring-Generative-AI-with-GAN-Models-13-2048.jpg)
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Exploring Generative AI with GAN Models
- 1. Let’s Start Lakshay Kumar (lakshaykumar.tech/)lakshaykumar.tech/linkedin
- 2.
- 3. Exploring Generative AIwith GAN Models “When it comes to Generative AI, the robots are no longer just good at math, they're also fantastic artists! Let’s Explore 01 Generative AI & GANs 02 Working & Training GANs 03 Industrial Applications lakshaykumar.tech
- 4. GAN Models GANs andLLMs are the subfield of Deep Learning. Set of two algorithms competing with each other. Based on self-supervisedly trained models. Requires a enormous amount of labeled data Generative Adversarial Networks
- 5. GAN Algorithm “Data isfake” “No, this data is real” Discriminator “Enormous!” Data “Support the generator” Noise Generated and Real one Sample Generator
- 6.
- 7. Linear and LogisticRegression IMP!
- 8. Real Sample Linear Regression y x y= wx + b y = output (prediction) w = weights x = input data (unseen) b = Bias (constant)
- 9. Real Sample Logistic Regression y x 𝑆𝑦 = 1 1 + ⅇ−𝑦 x = variable (prediction) Range of S(y) is (0,+1) Sigmoid function
- 10. How do generatorand discriminator works?
- 11. 1 2 3 4 GANs are MathematicallyRepresented by : 𝒎𝒊𝒏~𝑮, 𝒎𝒂𝒙~𝑫 𝑽 𝑫, 𝑮 = 𝑬𝒙~𝑷𝒅𝒂𝒕𝒂 𝒙 𝐥𝐨𝐠𝐃 𝐱 + 𝐄 𝐳~𝐩 𝐳 [𝐥𝐨𝐠(𝟏 − 𝐃 𝐆 𝐳 ] Minimizing Generator Maximizing Discriminator Expectation value over the probability distribution of real sample Log of discriminator output on real data Expectation value over the probability distribution of random noise Log of compliment of Discriminator output of Generator from Random Noise (z). This makes sure that generator produces such a output that is difficult for discriminator to catch
- 12. 1 2 3 4 “This data isfrom real sample” - Discriminator Generator Generated Sample Real Sample Discriminator 𝒎𝒊𝒏~𝑮, 𝒎𝒂𝒙~𝑫 𝑽 𝑫, 𝑮 = 𝑬𝒙~𝑷𝒅𝒂𝒕𝒂 𝒙 𝐥𝐨𝐠𝐃 𝐱 + 𝐄 𝐳~𝐩 𝐳 [𝐥𝐨𝐠(𝟏 − 𝐃 𝐆 𝐳 ] Dominant Discriminator’s Loss function makes the generator’s learning difficult!
- 13. 1 2 3 4 “No, I generatedthis data” - Generator Generated Sample Generator Discriminator 𝒎𝒊𝒏~𝑮, 𝒎𝒂𝒙~𝑫 𝑽 𝑫, 𝑮 = 𝑬𝒙~𝑷𝒅𝒂𝒕𝒂 𝒙 𝐥𝐨𝐠𝐃 𝐱 + 𝐄 𝐳~𝐩 𝐳 [𝐥𝐨𝐠(𝟏 − 𝐃 𝐆 𝐳 ] Equilibrium balance where generator fools the discriminator
- 14. 1 2 3 4 Implementation Loss : Binarycross Entropy Optimisation : Adam Generator, G Discriminator, D D’ updates Real Samples Noise Generated Sample D_loss_real D_loss_fake D_loss + = Back propagation : Compute Gradient Update Discriminator weights G’ updates Real Samples Noise Generated Sample D_output G_sample G_loss ~ = Back propagation : Compute Gradient Update Genrator weights Epoch Save the Model
- 15.
- 16. Types of GenerativeAdversarial Networks Vanilla GANs Deep Convolutional GANs Conditional GANs Super Resolution GANs Simplest form of GANS where generator and discriminator are simple multi-layer neural network unit that does certain computations to detect features or business intelligence in the input data. Text Generation Data Augmentation
- 17. Types of GenerativeAdversarial Networks Vanilla GANs Deep Convolutional GANs Conditional GANs Super Resolution GANs Combination of GANs and CNNs DCGANs use convolutional layers to generate realistic images by learning patterns and features from a training dataset. The generator network up samples random noise into images, while the discriminator network tries to distinguish between real and generated images. Realistic Faces generation Deep Fake Images generation Generating variations of images
- 18. Types of GenerativeAdversarial Networks Vanilla GANs Deep Convolutional GANs Conditional GANs Super Resolution GANs Extension of GANs CGANs take input noise along with conditional labels to generate realistic samples. CGANs take input noise along with conditional labels to generate realistic samples from same class. This generates more targeted and controlled outputs. Image to Image Translation Text to image synthesis
- 19. Types of GenerativeAdversarial Networks Vanilla GANs Deep Convolutional GANs Conditional GANs Super Resolution GANs Increases the image resolution The generator takes a low-resolution image as input and generates a high-resolution image. The discriminator then tries to distinguish between the generated high-resolution images and real high-resolution images. This adversarial training process improves the generator's ability to produce high-quality, visually appealing, and realistic super-resolved images Image Resolution Enhancer
- 20.
- 21. Industrial Applications #1 HealthCare #2 Gaming #3 Fashion #4 Marketing #5 Manufacturing Medicalimage synthesis for enhanced diagnosis and treatment planning. Drug discovery and development through generative models. Generating synthetic patient data for privacy-preserving research. Augmenting medical education with interactive virtual simulations.
- 22. Industrial Applications #1 HealthCare #2 Gaming #3 Fashion #4 Marketing #5 Manufacturing AI-assistedgame design and development tools for faster iteration. Procedural generation of game environments and levels. AI-generated music and sound effects for immersive experiences. AI-generated non- player characters (NPCs) with realistic behaviors
- 23. Industrial Applications #1 HealthCare #2 Gaming #3 Fashion #4 Marketing #5 Manufacturing Customizedpattern generation for unique garment production. Virtual fashion try-on for personalized shopping experiences. AI-generated designs for trend forecasting and inspiration. Automated fashion styling recommendations for individual customers.
- 24. Industrial Applications #1 HealthCare #2 Gaming #3 Fashion #4 Marketing #5 Manufacturing Chatbotand virtual assistant integration for enhanced customer support. Personalized content creation for targeted marketing campaigns. Automated ad generation for increased efficiency and scalability. AI-powered recommendation systems for personalized product suggestions.
- 25. Industrial Applications #1 HealthCare #2 Gaming #3 Fashion #4 Marketing #5 Manufacturing Processoptimization: AI-based optimization of manufacturing processes. Product design : AI- generated prototypes and designs. Automated inspection: AI-powered visual inspection for defects. Predictive maintenance: Identifying equipment failures before they occur.
- 26.