The Adversarial Autoencoder (AAE) model (Fig. ) uses the same GRU model as the RNN but exchanges the re-parameterized latent space proposed in Kingma and Welling for an adversarial counterpart . As proposed in this model regularizes the latent space with an attached neural network, named the discriminator, that matches the aggregated posterior q(z) to the desired prior p(z), which in this model is a Gaussian prior. The discriminator is a fully connected network with 256 hidden layers, each with a fixed hidden vector of size 640. The encoder network acts as the embedder for the decoder and the "generator" for the discriminator. The decoder learns to reconstruct original data, as in the RNN model case, and the discriminator discriminates between an ideal prior, modeled by a vector filled with normally distributed noise as a proxy for a Gaussian prior, and the current latent vector. This model is hypothesised to perform well on reconstruction tasks given the relative success of adversarial models on the task of image generationKarras et al. .