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.
Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice.
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