Reviews: On Adversarial Mixup Resynthesis

This paper proposes a method for enhancing the latent space learned by auto-encoders, so that the learned latent space produces meaningful features useful for downstream tasks. The proposed approach considers interpolations in the latent space and encourages the reconstructions from these interpolations to be similar to the data using adversarial learning. The learned latent space is shown to capture useful feature via experiments on MNIST, KMNIST and SVHN. The paper presents some promising preliminary experiments. However, there are many issues in the experimental setup Why is the quality of features measured during training?