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Apple Publishes AI Research Paper on Using Adversarial Training to Improve Realism of Synthetic Imagery

Earlier this month Apple pledged to start publicly releasing its research on artificial intelligence. During the holiday week, Apple has released its first AI research paper detailing how its engineers and computer scientists used adversarial training to improve the typically poor quality of synthetic, computer game style images, which are frequently used to help machines learn.

The paper's authors are Ashish Shrivastava, a researcher in deep learning, Tomas Pfister, another deep learning scientist at Apple, Wenda Wang, Apple R&D engineer, Russ Webb, a Senior Research Engineer, Oncel Tuzel, Machine Learning Researcher and Joshua Susskind, who co-founded Emotient in 2012 and is a deep learning scientist.

The team describes their work on improving synthetic images to improve overall machine learning:

With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations. However, learning from synthetic images may not achieve the desired performance due to a gap between synthetic and real image distributions. To reduce this gap, we propose Simulated+Unsupervised (S+U) learning, where the task is to learn a model to improve the realism of a simulator's output using unlabeled real data, while preserving the annotation information from the simulator.

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