Learning in models with discrete latent variables is challenging due to high variance gradient estimators.
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.
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
Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice.
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