CS5720 - Week 8
Slide 148 of 160

GAN Applications and Examples

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Image Generation

Generate photorealistic images of faces, objects, scenes, and artwork that never existed before.
Faces Artwork Landscapes
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Style Transfer

Transform images to adopt the artistic style of famous painters or create entirely new visual styles.
CycleGAN Neural Style Artistic
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Data Augmentation

Generate synthetic training data to improve model performance when real data is limited or expensive.
Medical Rare Events Synthetic
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Face Editing

Modify facial attributes like age, expression, hair color, or generate entirely new identities.
Age Expression Identity
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Super Resolution

Enhance low-resolution images to high-resolution with realistic details and textures.
SRGAN ESRGAN Real-ESRGAN
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Text-to-Image

Generate images from text descriptions, bridging the gap between natural language and visual content.
DALL-E Midjourney Stable Diffusion

GAN Evolution Timeline

2014
Original GAN
Ian Goodfellow introduces GANs
2016
DCGAN
Deep Convolutional GANs
2017
CycleGAN
Unpaired Image Translation
2018
StyleGAN
Style-based Generation
2021
DALL-E
Text-to-Image Generation
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Creative Industries
Revolutionizing art, design, gaming, and entertainment with AI-generated content and tools.
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Scientific Research
Enabling new research in medicine, materials science, and simulation through synthetic data.
Prepared by Dr. Gorkem Kar