May 2019
Intermediate to advanced
272 pages
7h 19m
English
Content preview from Hands-On Generative Adversarial Networks with Keras
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Image inpainting
The impactful and amazing work done by Guilin et al. on image inpainting has been featured on both Forbes and Fortune:
Image inpainting
In their paper, the authors claim that using regular convolutions for image inpainting led to issues such as color discrepancy and blurriness, and that postprocessing methods to reduce such artifacts are not efficient. Their solution to this problem is through partial convolution, in which the convolution is masked and normalized with respect to the pixels that are valid.
In the visual arts, this work represents a new avenue for exploring and synthesizing content using a smart retouching brush. ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.