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However, it is still an open question whether humans are prone to similar mistakes.
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Special thanks also goes to computer vision specialist Rebecca Bur Wei for generously offering her expertise in editing and revising drafts of this article.
In this paper we introduce the building blocks for constructing spherical CNNs.
We propose a definition for the spherical cross-correlation that is both expressive and rotation-equivariant.
The approach demonstrates its effectiveness for classifying 3D shapes and Spherical MNIST images as well as for molecular energy regression, an important problem in computational chemistry.
Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich.
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Photorealistic image stylization concerns transferring style of a reference photo to a content photo with the constraint that the stylized photo should remain photorealistic.
While several photorealistic image stylization methods exist, they tend to generate spatially inconsistent stylizations with noticeable artifacts.