GAN is a generative ML model that is widely used in advertising, games, entertainment, media, pharmaceuticals, and other industries. You can use it to create fictional characters and scenes, simulate facial aging, change image styles, produce chemical formulas synthetic data, and more.
For example, the following images show the effect of picture-to-picture conversion.

The following images show the effect of synthesizing scenery based on semantic layout.

This post walks you through building your first GAN model using Amazon SageMaker. This is a journey of learning GAN from the perspective of practical engineering experiences, as well as opening a new AI/ML domain of generative models.
We also introduce a use case of one of the hottest GAN applications in the synthetic data generation area. We hope this gives you a tangible sense on how GAN is used in real-life scenarios.
Overview of solution
Among the following two pictures of

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