Generative Adversarial Networks (GANs)

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Mentor Based

Program Syllabus

Module 1

About This Course

This program explores the theory and applications of GANs, focusing on their two-component structure (generator and discriminator). Participants will learn how GANs work, delve into advanced variants like DCGAN and CycleGAN, and implement practical projects using GANs for real-world problems such as image synthesis and data generation.

Module 2

Aim

To equip PhD scholars, researchers, and AI professionals with an in-depth understanding of Generative Adversarial Networks (GANs) and their practical applications. This course covers the fundamentals, advanced techniques, and hands-on experience in building and optimizing GANs for image generation, data augmentation, and creative AI.

Module 3

Program Objectives

Learn the theory behind GANs and how they function.
Build and train various types of GANs for image generation and creative applications.
Master advanced GAN techniques like DCGAN, CycleGAN, and Wasserstein GAN.
Solve real-world problems using GANs, including data augmentation and style transfer.
Understand and address the challenges of training GANs.

Module 4

Program Structure

Introduction to GANs

What are GANs?

Historical Context and Importance of GANs

Overview of Adversarial Networks (Generator vs. Discriminator)

GAN Architecture

Building Blocks of GANs

Loss Functions for GANs (Minimax Game)

Training GANs: Key Challenges and Solutions

Training Dynamics of GANs

Mode Collapse and Vanishing Gradients

Techniques to Stabilize GAN Training

GAN Evaluation Metrics (e.g., Inception Score, FID)

Deep Dive into Variants of GANs

Conditional GANs (cGANs)

Deep Convolutional GANs (DCGANs)

Wasserstein GANs (WGANs)

Progressive GANs

Applications of GANs

Image Generation

Text-to-Image Synthesis

Video and Audio Generation

Advanced Topics in GANs

CycleGANs for Image-to-Image Translation

StyleGAN and Style Transfer

GANs in Data Augmentation and Privacy Preservation

Ethical Implications of GANs

Deepfakes and Their Impact

Ethical Considerations in Using GANs

Mitigating Harm in GAN Applications

GANs in Research and Industry

Recent Developments in GAN Research

Applications in Art, Healthcare, and Entertainment

Deploying GAN Models in Production (Cloud/Edge)

Hands-on GANs with PyTorch/TensorFlow

Implementing Basic GANs in PyTorch

Customizing and Tuning GAN Architectures

Training GANs on Custom Datasets

Generative Adversarial Networks (GANs)

Course Overview

Program Syllabus

About This Course

Aim

Program Objectives

Program Structure

Lead Instructor

Dr. Sarah Chen