PixelVAE++: Improved PixelVAE with Discrete Prior

By Hossein Sadeghi, Evgeny Andriyash, Walter Vinci, Lorenzo Buffoni, Mohammad H. Amin. 2019

34 citations recorded by the source corpus.

Constructing powerful generative models for natural images is a challenging task. PixelCNN models capture details and local information in images very well but have limited receptive field. Variational autoencoders with a factorial decoder can capture global information easily, but they often fail to reconstruct details faithfully. PixelVAE combines the best features of the two models and constructs a generative model that is able to learn local and global structures. Here we introduce PixelVAE++, a VAE with three types of latent variables and a PixelCNN++ for the decoder. We introduce a novel architecture that reuses a part of the decoder as an encoder. We achieve the state of the art performance on binary data sets such as MNIST and Omniglot and achieve the state of the art performance on CIFAR-10 among latent variable models while keeping the latent variables informative.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Generative Models

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

In Generative Models

Citations in the corpus

Score-based Generative Modeling in Latent Space (2021, background): “[91] Hossein Sadeghi, Evgeny Andriyash, Walter Vinci, Lorenzo Buffoni, and Mohammad H Amin.”
NVAE: A Deep Hierarchical Variational Autoencoder (2020, background): “Moreover, the neural networks for VAEs should model long-range correlations in data [33, 34, 35], requiring the networks to have large receptive ﬁelds.”
Efficient-VDVAE: Less is more (2022, background): “Deep autoregressive models[1, 2, 3, 4] have long achieved the best log likelihoods across different modalities.”