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Poster
Fader Networks:Manipulating Images by Sliding Attributes
Guillaume Lample · Neil Zeghidour · Nicolas Usunier · Antoine Bordes · Ludovic DENOYER · Marc'Aurelio Ranzato

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #114 #None

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 applications where users can modify an image using sliding knobs, like faders on a mixing console, to change the facial expression of a portrait, or to update the color of some objects. Compared to the state-of-the-art which mostly relies on training adversarial networks in pixel space by altering attribute values at train time, our approach results in much simpler training schemes and nicely scales to multiple attributes. We present evidence that our model can significantly change the perceived value of the attributes while preserving the naturalness of images.

Author Information

Guillaume Lample (Facebook AI Research)
Neil Zeghidour (Facebook A.I. Research / Ecole Normale Supérieure)
Nicolas Usunier (Facebook AI Research)
Antoine Bordes (Facebook AI Research)
Ludovic DENOYER (Universite Pierre et Marie Curie - Paris)
Marc'Aurelio Ranzato (Facebook)

Marc'Aurelio Ranzato is a research scientist and manager at the Facebook AI Research lab in New York City. His research interests are in the area of unsupervised learning, continual learning and transfer learning, with applications to vision, natural language understanding and speech recognition. Marc'Aurelio has earned a PhD in Computer Science at New York University under Yann LeCun's supervision. After a post-doc with Geoffrey Hinton at University of Toronto, he joined the Google Brain team in 2011. In 2013 he joined Facebook and was a founding member of the Facebook AI Research lab.

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