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FONTS: A Synthetic MNIST-Like Dataset with Known Manifold

In deep learning and computer vision, data is often assumed to lie on a low-dimensional manifold, embedded within the potentially high-dimensional input space — as, for example, for images. However, the manifold is usually not known which hinders deeper understanding of many phenomena in deep learning, such as adversarial examples. Based on my recent CVPR’19 paper, I want to present FONTS, a MNIST-like, synthetically created dataset with known manifold to study adversarial example.

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