Google’s latest artificial intelligence experiment is taking in Street View imagery from Google Maps and transforming it into professional-grade photography through post-processing — all without a human touch. Hui Fang, a software engineer on Google’s Machine Perception team, says the project uses machine learning techniques to train a deep neural network to scan thousands of Street View images in California for shots with impressive landscape potential. The software then “mimics the workflow of a professional photographer” to turn that imagery into an aesthetically pleasing panorama.
The research, posted to the pre-print server arXiv earlier this week, is a great example of how AI systems can be trained to perform tasks that aren’t binary, with a right or wrong answer, and more subjective, like in the fields of art and photography. Doing this kind of aesthetic training with software can be labor-intensive and time-consuming, as it has traditionally required labeled data sets. That means human beings have to manually pick out which lighting effects or saturation filters, for example, result in a more aesthetically pleasing photograph.
Fang and his team used a different method. They were able to train the neural network quickly and efficiently to identify what most would consider superior photographic elements using what’s known as a generative adversarial network. This is a relatively new and promising technique in AI research that pits two neural networks against one another and uses the results to improve the overall system.Read More...
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