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Neural Photo Editor works like magic thanks to machine learning

Neural Photo Editing with Introspective Adversarial Networks
Neural Photo Editor is an experimental piece of retouching software from researchers at the University of Edinburgh that uses neural networks to act like Photoshop on steroids. Thanks to machine learning, it can intuitively interpret how a user intends to retouch a photo based on a “contextual paintbrush.” A single brush can change hair color, fill in bald spots, or add a toothy grin.

The process couldn’t be simpler: Users select a color for their paintbrush and the system analyzes that color in context with the image in order to produce an intelligent output. Painting over a subject’s mouth with a white brush, for example, can make a smile larger, while painting with a dark color on a forehead can add bangs.

The software currently works best on images generated by it that have constrained limits on what can be manipulated. However, through the use of “introspective adversarial networks,” an advanced masking system allows it to be applied to existing images, as well.

The tech-savvy can download Neural Photo Editor from Github, but don’t go canceling your Adobe Creative Cloud subscription anytime soon. The tool is very much in its early days and is far from perfect. While it produces stable outcomes much of the time, it can still lead to bizarre results on occasion, as the video above demonstrates. The video also shows the system working with very low-resolution images, so the practical applications for such a tool for photographers are currently nonexistent.

Still, it is an intriguing look at how photo editing could evolve in the future to be smarter and more context-aware, significantly increasing the speed at which a photo could be majorly retouched. It also represents a practical application for the general use of neural networks in personal computing.

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