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• W3168596171 abstract "We show how to learn a map that takes a content code, derived from a face image, and a randomly chosen style code to an anime image. We derive an adversarial loss from our simple and effective definitions of style and content. This adversarial loss guarantees the map is diverse -- a very wide range of anime can be produced from a single content code. Under plausible assumptions, the map is not just diverse, but also correctly represents the probability of an anime, conditioned on an input face. In contrast, current multimodal generation procedures cannot capture the complex styles that appear in anime. Extensive quantitative experiments support the idea the map is correct. Extensive qualitative results show that the method can generate a much more diverse range of styles than SOTA comparisons. Finally, we show that our formalization of content and style allows us to perform video to video translation without ever training on videos." @default.
• W3168596171 created "2021-06-22" @default.
• W3168596171 creator A5009262110 @default.
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• W3168596171 date "2021-06-11" @default.
• W3168596171 modified "2023-09-27" @default.
• W3168596171 title "GANs N' Roses: Stable, Controllable, Diverse Image to Image Translation (works for videos too!)" @default.
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