Glow
E736216
Glow is a generative flow-based model architecture used for high-quality image and audio synthesis through invertible transformations.
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
NERFINISHED
ⓘ
VAEs ⓘ |
| extends | RealNVP NERFINISHED ⓘ |
| field | machine learning ⓘ |
| hasAbbreviation | Glow NERFINISHED ⓘ |
| hasArchitectureComponent |
coupling layers
ⓘ
invertible 1x1 convolution layers ⓘ split operations ⓘ squeezing operations ⓘ |
| hasAuthor |
Diederik P. Kingma
NERFINISHED
ⓘ
Prafulla Dhariwal NERFINISHED ⓘ |
| 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 NERFINISHED ⓘ |
| hasTrainingObjective | maximum likelihood estimation ⓘ |
| implementedIn |
PyTorch
NERFINISHED
ⓘ
TensorFlow NERFINISHED ⓘ |
| improvesOver | RealNVP NERFINISHED ⓘ |
| publishedAt | International Conference on Machine Learning NERFINISHED ⓘ |
| subfield | deep generative modeling ⓘ |
| supports | exact latent-variable inference ⓘ |
| usedFor |
image editing
ⓘ
image generation ⓘ image synthesis ⓘ latent space interpolation ⓘ representation learning ⓘ |
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.