  The field of deep generative modeling is dominated by generative adversarial
networks (GANs). However, the training of GANs often lacks stability, fails to
converge, and suffers from model collapse. It takes an assortment of tricks to
solve these problems, which may be difficult to understand for those seeking to
apply generative modeling. Instead, we propose two novel generative
autoencoders, AE-OTtrans and AE-OTgen, which rely on optimal transport instead
of adversarial training. AE-OTtrans and AEOTgen, unlike VAE and WAE, preserve
the manifold of the data; they do not force the latent distribution to match a
normal distribution, resulting in greater quality images. AEOTtrans and
AE-OTgen also produce images of higher diversity compared to their predecessor,
AE-OT. We show that AE-OTtrans and AE-OTgen surpass GANs in the MNIST and
FashionMNIST datasets. Furthermore, We show that AE-OTtrans and AE-OTgen do
state of the art on the MNIST, FashionMNIST, and CelebA image sets comapred to
other non-adversarial generative models.

Machine Learning;Computer Vision and Pattern Recognition