Generative Adversarial Networks (GANs) Course – 4 weeks

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

This program aims 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

Module 1: Introduction to GANs

What are GANs?
Historical Context and Importance of GANs
Overview of Adversarial Networks (Generator vs. Discriminator)

Module 2: GAN Architecture

Building Blocks of GANs
Loss Functions for GANs (Minimax Game)
Training GANs: Key Challenges and Solutions

Module 3: Training Dynamics of GANs

Mode Collapse and Vanishing Gradients
Techniques to Stabilize GAN Training
GAN Evaluation Metrics (e.g., Inception Score, FID)

Module 4: Deep Dive into Variants of GANs

Conditional GANs (cGANs)
Deep Convolutional GANs (DCGANs)
Wasserstein GANs (WGANs)
Progressive GANs

Module 5: Applications of GANs

Image Generation
Text-to-Image Synthesis
Video and Audio Generation

Module 6: Advanced Topics in GANs

CycleGANs for Image-to-Image Translation
StyleGAN and Style Transfer
GANs in Data Augmentation and Privacy Preservation

Module 7: Ethical Implications of GANs

Deepfakes and Their Impact
Ethical Considerations in Using GANs
Mitigating Harm in GAN Applications

Module 8: GANs in Research and Industry

Recent Developments in GAN Research
Applications in Art, Healthcare, and Entertainment
Deploying GAN Models in Production (Cloud/Edge)

Module 9: 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.

Program Deliverables

Access to e-LMS
Real-Time Project for Dissertation
Project Guidance
Paper Publication Opportunity
Self Assessment
Final Examination
e-Certification
e-Marksheet

Future Career Prospects

AI Research Scientist
Deep Learning Engineer
GAN Specialist
Data Augmentation Expert
Computer Vision Engineer
Creative AI Developer

Job Opportunities

AI-driven startups
Creative industries using generative AI
Research labs focusing on computer vision and generative models
Data science teams for synthetic data generation
Gaming and virtual environment companies
Animation and media production firms

MODE	Online/ e-LMS
TYPE	Self Paced
LEVEL	Moderate
DURATION	4 weeks

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Generative Adversarial Networks (GANs) Course – 4 weeks

Aim

Program Objectives