Earlier this month, Apple made a splash when it told the artificial intelligence research community that the secretive company would start publishing AI papers of its own. Not even a month later, it’s already started making good on that promise.

Apple has published its very first academic paper on AI on December 22. (The paper was submitted for publication on November 15.) The paper describes a technique for how to improve the training of an algorithm’s ability to recognize images using computer-generated images rather than real-world images......Continue Reading

According to paper, Apple develops a new method called Simulated+Unsupervised (S+U) learning since there is always a gap between synthetic and real image distributions. ''Synthetic data is often not realistic enough, leading the network to learn details only present in synthetic images and fail to generalize well on real images. One solution to closing this gap is to improve the simulator. However, increasing the realism is often computationally expensive, the renderer design takes a lot of hard work, and even top renderers may still fail to model all the characteristics of real images. This lack of realism may cause models to overfit to ‘unrealistic’ details in the synthetic images,'' says the paper. 

Simulated+Unsupervised (S+U) learning. The task is to learn a model that improves the realism of synthetic images from a simulator using unlabeled real data, while preserving the annotation information. Image: Learning from Simulated and Unsupervised Images through Adversarial Training by Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Josh Susskind, Wenda Wang, Russ Webb Apple Inc.

Top image © Gabrielle Lurie/AFP/Getty Images

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