 Model description  The Face Frontalization model is the Generator part of a [GAN](https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf) that was trained in a supervised fashion on profile-frontal image pairs. The Discriminator was based on a fairly standard [DCGAN](https://arxiv.org/abs/1511.06434) architecture, where the input is a 128x128x3 image that is processed through multiple convolutional layers, to be classified as either Real or Fake. The Generator had to be modified in order to fit the supervised learning scenario. It consists of convolutional layers (the Encoder of the input image), followed by a 512-dimensional hidden representation that is then fed into the Decoder made up of deconvolutional layers, which produces the output image. For more details on the model's architecture, see [this blog post](https://blog.scaleway.com/gpu-instances-using-deep-learning-to-obtain-frontal-rendering-of-facial-images/).  