|
gptkbp:instance_of
|
gptkb:television_channel
|
|
gptkbp:bfsLayer
|
5
|
|
gptkbp:bfsParent
|
gptkb:Pixel_CNN
|
|
gptkbp:applies_to
|
image generation
|
|
gptkbp:based_on
|
gptkb:Pixel_CNN
|
|
gptkbp:can_create
|
high-quality images
|
|
gptkbp:developed_by
|
gptkb:philosopher
|
|
gptkbp:has_achievements
|
state-of-the-art results
|
|
gptkbp:has_programs
|
gptkb:Artificial_Intelligence
gptkb:software_framework
image processing
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Gated Pixel CNN
|
|
gptkbp:improves
|
Pixel CNN's performance
|
|
gptkbp:introduced
|
gptkb:2016
|
|
gptkbp:is_cited_in
|
numerous academic papers
|
|
gptkbp:is_compared_to
|
gptkb:DALL-E
gptkb:Pixel_SNAIL
gptkb:Style_GAN
|
|
gptkbp:is_evaluated_by
|
gptkb:CIFAR-10
gptkb:Celeb_A
gptkb:MNIST
log-likelihood
|
|
gptkbp:is_explored_in
|
tutorials
workshops
online courses
various research studies
conference presentations
thesis works
|
|
gptkbp:is_implemented_in
|
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch
|
|
gptkbp:is_influenced_by
|
gptkb:Generative_Adversarial_Networks
gptkb:Variational_Autoencoders
|
|
gptkbp:is_known_for
|
high-resolution outputs
flexibility in architecture
pixel-level generation
|
|
gptkbp:is_optimized_for
|
parallel processing
scalability
|
|
gptkbp:is_part_of
|
deep learning
open-source projects
collaborative research efforts
AI research initiatives
neural network architectures
Deep Learning literature
|
|
gptkbp:is_related_to
|
autoregressive models
|
|
gptkbp:is_supported_by
|
gptkb:Research_Institute
academic institutions
funding agencies
industry partners
|
|
gptkbp:is_used_for
|
conditional image generation
unconditional image generation
|
|
gptkbp:is_used_in
|
computer vision
|
|
gptkbp:training
|
maximum likelihood estimation
|
|
gptkbp:uses
|
convolutional neural networks
|
|
gptkbp:utilizes
|
gated activation functions
|