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UCSD & Microsoft Improve Image Recognition With Extremely Low FLOPs

A research team from the University of California San Diego and Microsoft has come up with a novel approach to improve model accuracy on computer vision tasks at extremely low compute costs, achieving significant performance gains over state-of-the-art models.

Today’s increasingly efficient CNN architectures such as MobileNet and ShuffleNet have…

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