Reviews: ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching

Adversarial Feature Learning) is an interesting extension to GANs, which can be used to train a generative model by learning generator G(z) and inference E(x) functions, where G(z) maps samples from a latent space to data and E(x) is an inference model mapping observed data to the latent space. This model is trained adversarially by jointly training E(x) and G(z) with a discriminator D(x,z) which is trained to distinguish between real (E(x), x) samples and fake (z, G(z)) samples. This is an interesting approach and has been shown to generate latent representations which are useful for semi-supervised learning. The authors highlight an issue with the ALI model, by constructing a small example for which there exist optimal solutions to the ALI loss function which have poor reconstruction, i.e. G(E(x)) can be very different to x.