Generative Adversarial Networks (GANs)

About Course

This course offers an in-depth exploration of Generative
Adversarial Networks (GANs), a revolutionary class of
deep learning models capable of synthesizing new data
instances that closely mimic a given training dataset.
Students will dive into the theoretical foundations of
GANs and gain hands-on experience implementing
various GAN architectures for diverse applications,
including high-resolution image synthesis, realistic
video generation, unique artistic style transfer, and the
creation of synthetic data for privacy-sensitive
scenarios

Grasp Fundamental Principles: Comprehend the core principles and mathematical underpinnings of GANs, including minimax game theory and their specific loss functions.
Implement Advanced Architectures: Effectively implement and train cutting-edge GAN architectures such as DCGAN, StyleGAN2, and CycleGAN from scratch.
Apply Across Domains: Apply GANs across diverse domains, focusing on generating hyper-realistic images, synthesizing coherent text, and creating
novel audio samples.
Overcome Training Challenges: Address common challenges in GAN training and evaluation, such as mode collapse, vanishing gradients, and training
instability, by utilizing advanced regularization techniques.
Develop Controllable Generation: Develop robust solutions for conditional and controllable generation, enabling tasks like text-to-image synthesis or attribute manipulation (e.g., altering hair color on a generated face).
Evaluate Generated Content: Evaluate the quality, diversity, and ethical implications of generated content using quantitative metrics like FID and Inception Score, alongside qualitative human perception studies.

Course Content

GAN Fundamentals
Explore the architecture of generator and discriminator networks, the dynamics of adversarial training, the minimax objective, and the concept of Nash equilibrium in GANs, including the role of Jensen-Shannon divergence. Understand their theoretical guarantees, limitations, historical development, and comparisons with other generative models like Variational Autoencoders (VAEs), Autoregressive Models (e.g., PixelRNN), and Flow-based Models.

GAN Architectures
Examine DCGAN for stable image generation, progressive growing GANs for high-resolution output, and StyleGAN/StyleGAN2 for photorealistic facial synthesis and artistic control. Learn about BigGAN for diverse large-scale image generation and CycleGAN for unpaired image translation (e.g., converting summer scenes to winter). Discuss architecture design considerations for specific real- world applications

Training and Optimization
Address common GAN training challenges such as mode collapse, non-convergence, and vanishing gradients. Explore various loss function variants, including Wasserstein GAN (WGAN) with Gradient Penalty (WGAN-GP), Least Squares GAN (LSGAN), and Boundary Equilibrium Generative Adversarial Networks (BEGAN). Master regularization techniques like spectral normalization and one-sided label smoothing, along with stabilization techniques such as two-time scale update rule (TTUR) and mini- batch discrimination for effective hyperparameter selection.

Conditional and Controllable Generation
Dive into Conditional GANs (cGANs) for generating specific classes or styles, including class-conditional generation. Explore advanced text-to-image synthesis using transformer-based models (e.g., StyleGAN-XL integrations) and fine-grained style manipulation/transfer. Understand attribute manipulation with GANs (e.g., changes in age, gender, or expression), disentangled representations, and how to build interactive generation systems for real- time creativity.

Evaluation and Metrics
Learn qualitative evaluation methods for generative models, including visual inspection and human perception studies. Master quantitative metrics such as Inception Score (IS), Fréchet Inception Distance (FID), Precision/Recall metrics, and Kernel Maximum Mean Discrepancy (KMMD). Explore advanced techniques for evaluating mode coverage and diversity, and robust comparison methodologies for various GAN variants

Applications and Future Directions
Discover advanced GAN applications in synthetic data augmentation for robust model training in privacy-sensitive domains, super-resolution for image clarity, image enhancement, inpainting complex missing regions, image restoration (e.g., old photo repair), and 3D object generation. Explore GANs for anomaly detection in cybersecurity, ethical considerations in synthetic media and deepfakes, and current research frontiers in generative modeling, including diffusion models and multimodal generation.

Capstone Project
Students will design and implement a GAN-based system for a chosen creative or practical application, pushing the boundaries of generative AI. This could involve developing a specialized image generation model for a niche dataset (e.g., historical artifacts), building an advanced style transfer system for video content, creating a complex synthetic data augmentation pipeline for medical imaging, or another innovative GAN application that addresses a specific real- world problem. The project requires documenting your chosen architecture, detailed training approach, advanced evaluation metrics, and compelling results with high-fidelity sample outputs and thorough performance analysis.

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Access cutting-edge deep learning frameworks such as TensorFlow 2.x and PyTorch with robust GAN implementations.
Utilize high-quality and diverse datasets for comprehensive training (e.g., CelebA-HQ, FFHQ, LSUN, COCO) and necessary high-performance GPU computing resources (e.g., NVIDIA V100 A100).
Gain proficiency with advanced evaluation tools and metrics beyond basic implementations, a comprehensive library of pre-trained models for comparative analysis and fine-tuning, and a curated collection of seminal research papers and recent arXiv preprints detailing state-of-the-art GAN techniques and breakthroughs.

Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs)

About Course

What Will You Learn?

Course Content

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Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs)

About Course

What Will You Learn?

Course Content

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