|
gptkbp:instanceOf
|
gptkb:model
|
|
gptkbp:application
|
data compression
image generation
semi-supervised learning
representation learning
anomaly detection
|
|
gptkbp:architecture
|
encoder-decoder
|
|
gptkbp:citation
|
high
|
|
gptkbp:component
|
latent space
decoder
encoder
|
|
gptkbp:field
|
gptkb:artificial_intelligence
gptkb:machine_learning
|
|
gptkbp:fullName
|
gptkb:Variational_Autoencoder
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
VAE
|
|
gptkbp:influenced
|
gptkb:beta-VAE
conditional VAE
disentangled representation learning
vector quantized VAE
|
|
gptkbp:input
|
data sample
|
|
gptkbp:introduced
|
gptkb:Diederik_P._Kingma
gptkb:Max_Welling
|
|
gptkbp:introducedIn
|
2013
|
|
gptkbp:latentVariableModel
|
true
|
|
gptkbp:lossFunction
|
gptkb:KL_divergence
reconstruction loss
|
|
gptkbp:objective
|
evidence lower bound
variational lower bound
|
|
gptkbp:openSource
|
gptkb:TensorFlow
gptkb:Keras
gptkb:PyTorch
|
|
gptkbp:output
|
probabilistic distribution
reconstructed sample
|
|
gptkbp:platform
|
Bayesian inference
|
|
gptkbp:publishedIn
|
gptkb:arXiv
|
|
gptkbp:relatedTo
|
gptkb:convolutional_neural_network
probabilistic graphical model
|
|
gptkbp:trainer
|
stochastic gradient descent
|
|
gptkbp:type
|
deep generative model
unsupervised model
|
|
gptkbp:usedFor
|
dimensionality reduction
unsupervised learning
generative modeling
data generation
|
|
gptkbp:uses
|
reparameterization trick
|
|
gptkbp:bfsParent
|
gptkb:Vereinigte_Arabische_Emirate
|
|
gptkbp:bfsLayer
|
6
|