Statements (52)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:Cloud_Computing_Service
|
| gptkbp:addresses |
mode collapse
|
| gptkbp:appliesTo |
machine learning
|
| gptkbp:developedBy |
Gulrajani_et_al.
|
| gptkbp:enhances |
stability of training
|
| gptkbp:hasImpactOn |
adversarial training
|
| https://www.w3.org/2000/01/rdf-schema#label |
WGAN-Gradient Penalty
|
| gptkbp:improves |
gptkb:Wasserstein_GAN
sample quality |
| gptkbp:isAttendedBy |
research institutions
tech companies |
| gptkbp:isBasedOn |
Wasserstein distance
Kantorovich-Rubinstein_duality |
| gptkbp:isCitedBy |
numerous research papers
|
| gptkbp:isDiscussedIn |
machine learning conferences
AI journals |
| gptkbp:isEvaluatedBy |
gptkb:ImageNet
gptkb:CIFAR-10 MNIST Fashion-MNIST CelebA LSUN |
| gptkbp:isInfluencedBy |
GANs
Variational Autoencoders normalizing flows |
| gptkbp:isLocatedIn |
gptkb:PyTorch
TensorFlow |
| gptkbp:isPartOf |
DCGAN
AI research community generative modeling computer vision domain GAN_family LSGAN SAGAN |
| gptkbp:isRelatedTo |
deep learning
|
| gptkbp:isSupportedBy |
NVIDIA GPUs
TPUs |
| gptkbp:isUsedFor |
data augmentation
text-to-image synthesis video generation image synthesis style transfer |
| gptkbp:isUsedIn |
unsupervised learning
transfer learning image generation semi-supervised learning |
| gptkbp:relatedTo |
traditional GANs
|
| gptkbp:requires |
hyperparameter tuning
Lipschitz_constraint |
| gptkbp:uses |
gradient penalty
|
| gptkbp:utilizes |
critic network
|
| gptkbp:yearEstablished |
2017
|