How is adversarial autoencoder better than an ordinary autoencoder?
In an adversarial autoencoder, the discriminator network can help generate a more suitable distribution of the latent parameters than would be produced through random selection. If the statistical distribution of the compressed representation is completely unaltered and done in a way as to maximize only the reconstruction accuracy, it is very hard to generate new, high-quality output images because we have to guess the distribution of these latent variables. In a variational autoencoder, this problem is solved by forcing the latent parameters (compressed representation) to conform to a normal (Gaussian) distribution, allowing us to easily generate new images. Using a discriminator to determine a suitable distribution increases the quality of the results produced because the constraints imposed are done in such a way that the reconstruction error can still remain very low and thus the output images are not blurry.