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by apstls
3666 days ago
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Recent research in deep manifold traversal may interest you [1]. This method uses deep neural networks to approximate the manifold of natural images, learns transformations that traverse the manifold from one image class to another (i.e. from images of young people to images of old people), and then allows you to transform any source image into a new, automatically-generated image by mapping it to the manifold and performing the transformation. So, for example, the paper's authors learn the transformation from images of young people to old people, and then are able to generate realistic-looking images of the celebrities with older facial features (as well as darken hair colors, change skin tones, and even colorize black and white images). This is somewhat analogous to the way that word2vec vectors allow you to do things like queen = king + (man - woman), with an image's projection onto the approximated manifold being analogous to a word's word2vec vector. [1] http://arxiv.org/pdf/1511.06421v3.pdf |
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