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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
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