VAE (Variational Autoencoder)
GPTKB entity
Statements (49)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:model
generative model |
| gptkbp:application |
data compression
drug discovery speech synthesis semi-supervised learning representation learning image synthesis molecular generation |
| gptkbp:architecture |
encoder-decoder
|
| gptkbp:citation |
high
|
| gptkbp:component |
probabilistic decoder
probabilistic encoder reparameterization trick |
| gptkbp:extendsTo |
FactorVAE
InfoVAE Beta-VAE Conditional VAE (CVAE) Discrete VAE Hierarchical VAE Vector-Quantized VAE (VQ-VAE) Wasserstein VAE |
| https://www.w3.org/2000/01/rdf-schema#label |
VAE (Variational Autoencoder)
|
| gptkbp:influenced |
deep generative models
representation learning research |
| gptkbp:introduced |
gptkb:Diederik_P._Kingma
gptkb:Max_Welling |
| gptkbp:introducedIn |
2013
|
| gptkbp:latentSpace |
continuous
|
| gptkbp:lossFunction |
gptkb:KL_divergence
reconstruction loss |
| gptkbp:objective |
evidence lower bound (ELBO)
variational lower bound |
| gptkbp:openSource |
gptkb:TensorFlow
gptkb:Keras gptkb:JAX gptkb:PyTorch |
| gptkbp:publishedIn |
arXiv preprint arXiv:1312.6114
|
| gptkbp:relatedTo |
gptkb:convolutional_neural_network
Bayesian inference GAN (Generative Adversarial Network) |
| gptkbp:trainer |
stochastic gradient descent
|
| gptkbp:usedFor |
image generation
dimensionality reduction unsupervised learning generative modeling anomaly detection |
| gptkbp:bfsParent |
gptkb:AudioLDM
|
| gptkbp:bfsLayer |
7
|