Fader Networks: Manipulating Images by Sliding Attributes

Understand

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.