Glow

E736216

Glow is a generative flow-based model architecture used for high-quality image and audio synthesis through invertible transformations.

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Glow canonical 1

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Statements (48)

Predicate Object
instanceOf deep generative model ⓘ
flow-based generative model architecture ⓘ
normalizing flow model ⓘ
appliedIn audio processing ⓘ
computer vision ⓘ
basedOn normalizing flows ⓘ
canBeAppliedTo audio synthesis ⓘ
speech modeling ⓘ
comparedWith GANs ⓘ
VAEs ⓘ
extends RealNVP ⓘ
field machine learning ⓘ
hasAbbreviation Glow ⓘ
hasArchitectureComponent coupling layers ⓘ
invertible 1x1 convolution layers ⓘ
split operations ⓘ
squeezing operations ⓘ
hasAuthor Diederik P. Kingma ⓘ
Prafulla Dhariwal ⓘ
hasEvaluationMetric bits per dimension ⓘ
log-likelihood ⓘ
hasInfluenced subsequent normalizing flow models ⓘ
hasKeyProperty efficient sampling ⓘ
exact log-likelihood computation ⓘ
invertible transformations ⓘ
parallelizable architecture ⓘ
tractable inference ⓘ
hasKeyTechnique actnorm layers ⓘ
affine coupling layers ⓘ
invertible 1x1 convolutions ⓘ
multi-scale architecture ⓘ
hasLatentSpace continuous latent variables ⓘ
hasProperty scalable to high-resolution images ⓘ
supports conditional generation ⓘ
hasPublicationYear 2018 ⓘ
hasTitle Glow: Generative Flow with Invertible 1x1 Convolutions ⓘ
hasTrainingObjective maximum likelihood estimation ⓘ
implementedIn PyTorch ⓘ
TensorFlow ⓘ
improvesOver RealNVP ⓘ
publishedAt International Conference on Machine Learning ⓘ
linked to: ICML
subfield deep generative modeling ⓘ
supports exact latent-variable inference ⓘ
usedFor image editing ⓘ
image generation ⓘ
image synthesis ⓘ
latent space interpolation ⓘ
representation learning ⓘ

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Referenced by (1)

Full triples — surface form annotated when it differs from this entity's canonical label.

WaveGlow → basedOn → Glow ⓘ