Generative Adversarial Networks (GANs)

Master the Power of GANs: Unleash Creativity and Solve Complex Problems with Generative AI

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MODE
Online/ e-LMS

TYPE
Self Paced

LEVEL
Moderate

DURATION
4 weeks

About

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.

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.

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.

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

Final Project

Students implement a novel GAN application (e.g., image style transfer, data augmentation, etc.)

Participant’s Eligibility

AI researchers, data scientists, and professionals with a background in machine learning, neural networks, or computer vision.

Program Outcomes

Ability to build and train advanced GAN models for real-world applications.
Proficiency in optimizing GANs for stable performance and high-quality outputs.
Deep understanding of how GANs can be applied in creative industries and data science.
Skills to address common GAN challenges like mode collapse and training instability.

Fee Structure

Standard Fee: INR ~~5,998~~ USD 90

Discounted Fee: INR 2,999 USD 45

We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!

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Key Takeaways

Program Assessment

Certification to this program will be based on the evaluation of following assignment (s)/ examinations:

Exam	Weightage
Mid Term Assignments	50 %
Project Report Submission (Includes Mandatory Paper Publication)	50 %

To study the printed/online course material, submit and clear, the mid term assignments, project work/research study (in completion of project work/research study, a final report must be submitted) and the online examination, you are allotted a 1-month period. You will be awarded a certificate, only after successful completion/ and clearance of all the aforesaid assignment(s) and examinations.