According to the litterature and specifically to this paper, the wasserstein autoencoders is an encoder-decoder architecture. So it must contain encoder and decoder parts.
in the algorithm description of GAN-based WAN, in the requirements, there were initialization of encoder, decoder and latent discriminator.
In parallel, I saw many implementations of GAN based WAE on github in tensorflow, there were all implemented through a discrminator and generator parts. No autoencoder architecture. So my question, are those implementations correspond to another architecture beside the WAE-GAN ? or maybe I didn't understand the connection between the paper and the implementation.
Here you will find some links for implementation: