Variational Autoencoders with Normalizing Flow Decoders
arXiv:2004.05617
Abstract
Recently proposed normalizing flow models such as Glow have been shown to be able to generate high quality, high dimensional images with relatively fast sampling speed. Due to their inherently restrictive architecture, however, it is necessary that they are excessively deep in order to train effectively. In this paper we propose to combine Glow with an underlying variational autoencoder in order to counteract this issue. We demonstrate that our proposed model is competitive with Glow in terms of image quality and test likelihood while requiring far less time for training.
References in corpus (9)
- Auto-Encoding Variational Bayes
- Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
- GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
- Pixel Recurrent Neural Networks
- Variational Inference with Normalizing Flows
- Variational Lossy Autoencoder
- Residual Flows for Invertible Generative Modeling
- PixelVAE: A Latent Variable Model for Natural Images
- Learnable Explicit Density for Continuous Latent Space and Variational Inference
Cited by in corpus (6)
- Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models
- VFlow: More Expressive Generative Flows with Variational Data Augmentation
- LHC Hadronic Jet Generation Using Convolutional Variational Autoencoders with Normalizing Flows
- Generative networks as inverse problems with fractional wavelet scattering networks
- Decoupling Global and Local Representations via Invertible Generative Flows
- There and Back Again: Unraveling the Variational Auto-Encoder