Reviews: Fader Networks:Manipulating Images by Sliding Attributes

These attributes are known at training time and for a dataset of faces include aspects like [old vs young], [smiling vs not smiling], etc. They hope to be able to tweak these attributes along a continuous spectrum, even when the labels only occur as binary values. To achieve this they propose an (encoder, decoder) setup where the encoder maps the image x to a latent vector z and then the decoder produces an image taking z, together with the attributes y as inputs. When such a network is trained in the ordinary fashion, the decoder learns to ignore y because z already encodes everything that the network needs to know. To compel the decoder network to use y, the authors propose introducing a adversarial learning framework in which a discriminator D is trained to infer the attributes from z. Thus the encoder must produce representations that are invariant to the attributes y. The writing is clear and any strong researcher should be able to reproduce their results from the presentation here.