Apple leaps into AI research with improved simulated + unsupervised learning

Apple leaps into AI research with improved simulated + unsupervised learning

Posted 6 hours ago by John Mannes (@JohnMannes)
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Corporate machine learning research may be getting a new vanguard in Apple. Six researchers from the company’s recently formed machine learning group published a paper that describes a novel method for simulated + unsupervised learning. The aim is to improve the quality of synthetic training images. The work is a sign of the company’s aspirations to become a more visible leader in the ever growing field of AI.

Google, Facebook, Microsoft and the rest of the techstablishment have been steadily growing their machine learning research groups. With

hundreds of publications each, these companies’ academic pursuits have been well documented, but Apple has been stubborn — keeping its magic all to itself.

Things started to change earlier this month when Apple’s Director of AI Research, Russ Salakhutdinov, announced that the company would soon begin publishing research. The team’s first attempt is both timely and pragmatic.

In recent times, synthetic images and videos have been used with greater frequency to train machine learning models. Rather than use cost and time intensive real-world imagery, generated images are less costly, readily available and customizable.

The technique presents a lot of potential, but it’s risky because small imperfections in synthetic training material can have serious negative implications for a final product. Put another way, it’s hard to ensure generated images meet the same quality standards as real images.

Apple is proposing to use Generative Adversarial Networks or GANs to improve the quality of these synthetic training images. GANs are not new, but Apple is making modifications to serve its purpose.

At a high level, GANs work by taking advantage of the adversarial relationship between competing neural networks. In Apple’s case, a simulator generates synthetic images that are run through a refiner. These refined images are then sent to a discriminator that’s tasked with distinguishing real images from synthetic ones. 

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