Search Results for author:
Found 2 papers, 1 papers with code
Neural Variational Dropout Processes
Learning to infer the conditional posterior model is a key step for robust meta-learning.
IB-GAN: Disentangled Representation Learning with Information Bottleneck GAN
IB-GAN objective is similar to that of InfoGAN but has a crucial difference; a capacity regularization for mutual information is adopted, thanks to which the generator of IB-GAN can harness a latent representation in disentangled and interpretable manner.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
