PixelGAN Autoencoders – Synced – Medium

This paper proposed a "PixelGAN Autoencoder", for which the generative path is a convolutional autoregressive neural network on pixels, conditioned on a latent code, and the recognition path uses a generative adversarial network (GAN) to impose a prior distribution on the latent code. This paper also shows different priors result in different decompositions of information between the latent code and the auto-regressive decoder. A Quick Review of GAN Generative Adversarial Network originally consists of one generator and one discriminator. The generator G samples the prior p(z) and generates the fake sample G(z) to maximally confuse the discriminator. The discriminator D(x) is trained to identify whether the input x is a sample from the real data distribution or a sample from the generative model.