5,002 research outputs found
Adaptive Density Estimation for Generative Models
Unsupervised learning of generative models has seen tremendous progress over
recent years, in particular due to generative adversarial networks (GANs),
variational autoencoders, and flow-based models. GANs have dramatically
improved sample quality, but suffer from two drawbacks: (i) they mode-drop,
i.e., do not cover the full support of the train data, and (ii) they do not
allow for likelihood evaluations on held-out data. In contrast,
likelihood-based training encourages models to cover the full support of the
train data, but yields poorer samples. These mutual shortcomings can in
principle be addressed by training generative latent variable models in a
hybrid adversarial-likelihood manner. However, we show that commonly made
parametric assumptions create a conflict between them, making successful hybrid
models non trivial. As a solution, we propose to use deep invertible
transformations in the latent variable decoder. This approach allows for
likelihood computations in image space, is more efficient than fully invertible
models, and can take full advantage of adversarial training. We show that our
model significantly improves over existing hybrid models: offering GAN-like
samples, IS and FID scores that are competitive with fully adversarial models,
and improved likelihood scores
Channel-Recurrent Autoencoding for Image Modeling
Despite recent successes in synthesizing faces and bedrooms, existing
generative models struggle to capture more complex image types, potentially due
to the oversimplification of their latent space constructions. To tackle this
issue, building on Variational Autoencoders (VAEs), we integrate recurrent
connections across channels to both inference and generation steps, allowing
the high-level features to be captured in global-to-local, coarse-to-fine
manners. Combined with adversarial loss, our channel-recurrent VAE-GAN
(crVAE-GAN) outperforms VAE-GAN in generating a diverse spectrum of high
resolution images while maintaining the same level of computational efficacy.
Our model produces interpretable and expressive latent representations to
benefit downstream tasks such as image completion. Moreover, we propose two
novel regularizations, namely the KL objective weighting scheme over time steps
and mutual information maximization between transformed latent variables and
the outputs, to enhance the training.Comment: Code: https://github.com/WendyShang/crVAE. Supplementary Materials:
http://www-personal.umich.edu/~shangw/wacv18_supplementary_material.pd
- …