Learning in models with discrete latent variables is challenging due to high variance gradient estimators.
We prove that, since the data instances with larger gradients play a more important role in the computation of information gain, GOSS can obtain quite accurate estimation of the information gain with a much smaller data size.
Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible.
#4 best model for Image Generation on LSUN Bedroom 256 x 256
Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications.
Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in individual domains.
#2 best model for Multimodal Unsupervised Image-To-Image Translation on Cats-and-Dogs
Our proposed method encourages bijective consistency between the latent encoding and output modes.
#2 best model for Multimodal Unsupervised Image-To-Image Translation on Edge-to-Shoes
Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice.