October 2017
Beginner to intermediate
270 pages
7h
English
Content preview from Machine Learning for Developers
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Types of GAN applications
GANs allow new applications to produce new samples from a previous set of samples, including completing missing information.
In the following screenshot, we depict a number of samples created with the LSGAN architecture on five scene datasets from LSUN, including a kitchen, church, dining room, and conference room:
LSGAN created models
Another really interesting example is class-conditional image sampling using the Plug and Play Generative Network (PPGN) to fill in 100 x 100 missing pixels in a real 227 x 227 image.
The following screenshot compares PPGN variations and the equivalent Photoshop image completion:
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