Controlling the Style of Images Generated using StyleGAN
Generative adversarial networks (GANs) are algorithmic architectures that use two neural networks, pitting one against the other (thus the “adversarial”) in order to generate new, synthetic instances of data that can pass for real data. They are used widely in image generation, video generation and voice generation
While GAN images become more realistic over time,one of the main challenges is controlling the output i.e:changing specific features such as pose,face shape and hair style. NVIDIA proposed a novel method which generated images starting from very low resolution and continuing to high resolution.By modifying the input at each level,it controls the visual features in that level from coarse features(pose,face shape) to find details(hair color without affecting other levels)
In this talk,I will be discussing how the lack of control affected the generation of images in previous GAN models and the changes introduced by StyleGAN that help in controlling the style and generating impressive images of synthetic human faces
Outline/Structure of the Demonstration
Agenda for the talk:
- Recap of GAN( 2 mins)
- Use cases of StyleGAN( 2 mins )
- StyleGAN Architecture( 10 mins)
- Code walkthrough: Image Generation with StyleGAN( 4 mins )
- Q / A session ( 2mins )
Learning Outcome
1.Why StyleGAN is a major breakthrough in the field of GAN
2.How it is used in Real life scenarios
3.Good understanding of the StyleGAN Architecture
Target Audience
Data Scientist, Deep Learning Engineers, Computer Vision Engineer, AI Researchers
Prerequisites for Attendees
Attendees are required to have a good understanding of basic ML and DL especially Convolutional Neural Networks and Generative Adversarial Networks
Video
Links
Links of my previous lectures and webinars:
https://www.youtube.com/watch?v=4eca8aA6t-A&t=16s
https://www.youtube.com/watch?v=Baf0FKIvA90
https://www.youtube.com/watch?v=MFrbynONvDQ
schedule Submitted 3 years ago
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