The accuracy of object recognition has dramatically increased due to deep learning architectures. For example, on the ImageNet benchmark, the top-five classification error rate plummeted from 28% in 2010 to 2.25% by 2017. Beyond benchmark datasets, this improved visual perception enables complex real-world applications such as diagnosing skin cancer from medical images and identifying birdsong.