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neural_style [2018/09/02 12:34]
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neural_style [2019/01/19 13:53] (current)
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 Encoder-decoder models are largely (a) uninterpretable,​ and (b) difficult to control in terms of their phrasing or content. This work proposes a neural generation system using a hidden semi-markov model (HSMM) decoder, which learns latent, discrete templates jointly with learning to generate. We show that this model learns useful templates, and that these templates make generation both more interpretable and controllable. ​ Encoder-decoder models are largely (a) uninterpretable,​ and (b) difficult to control in terms of their phrasing or content. This work proposes a neural generation system using a hidden semi-markov model (HSMM) decoder, which learns latent, discrete templates jointly with learning to generate. We show that this model learns useful templates, and that these templates make generation both more interpretable and controllable. ​
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 +https://​web.cs.hacettepe.edu.tr/​~karacan/​projects/​attribute_hallucination/#​ Manipulating Attributes of Natural Scenes via Hallucination
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 +https://​arxiv.org/​abs/​1810.01175 Line Drawings from 3D Models
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 +https://​hal.inria.fr/​hal-01802131v2/​document Unsupervised Learning of Artistic Styles with
 +Archetypal Style Analysis
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 +https://​compvis.github.io/​adaptive-style-transfer/​ A Style-Aware Content Loss for Real-time HD Style Transfer