Generative Adversarial Networks are a promising modern application of Deep Learning that allows models to *generate* examples. However, GANs are complex, difficult to tune, and limited to small examples. We will explore recent GAN progress with a model that generates faces conditional on desired features, like 'smiling' and 'bangs'.
This video workshop is designed for Data Scientists, researchers, and software developers familiar with keras, tensorflow, or similar recent Deep Learning tools. It is expected that most in the audience will be able to build models and begin to train them on a local machine. Such students will not leave the tutorial with fully trained models. While students are not expected to have remote access to a machine configured with CUDA and tensorflow-gpu, the instructor will.
After watching this video, students you should be able to
- Identify and explain the essential components of Generative Adversarial Networks including Deep Convolutional versions.
- Modify existing GAN implementations.
- Design a GAN for a novel application.
- Understand and explain recent improvements in GAN loss functions.
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