May 2019
Intermediate to advanced
272 pages
7h 19m
English
Content preview from Hands-On Generative Adversarial Networks with Keras
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GANGogh
GANGogh is the product of semester-long research performed by Kenny Jones and Derrick Bonafilia at Williams College in 2017.
In their project, the authors scoured the WikiArt database, which contains over 100,000 paintings, along with labels for style, genre, artist, and other features. The dataset that was used in their project contained 80,000 images with style and genre labels.
Their network setup is similar to the typical CGAN framework and is based on the improved WGAN. In the CGAN setup, the generator normally receives labels that are used to apply global conditioning on every layer; the discriminator, on the other hand, has an extra output that predicts the label of the input. In GANGogh, global conditioning was performed ...
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