Fader Networks: Manipulating Images by Sliding Attributes

@inproceedings{Lample2017FaderNM,
  title={Fader Networks: Manipulating Images by Sliding Attributes},
  author={Guillaume Lample and Neil Zeghidour and Nicolas Usunier and Antoine Bordes and Ludovic Denoyer and Marc'Aurelio Ranzato},
  booktitle={NIPS},
  year={2017}
}
This paper introduces a new encoder-decoder architecture that is trained to reconstruct images by disentangling the salient information of the image and the values of attributes directly in the latent space. As a result, after training, our model can generate different realistic versions of an input image by varying the attribute values. By using continuous attribute values, we can choose how much a specific attribute is perceivable in the generated image. This property could allow for… CONTINUE READING
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