|
gptkbp:instance_of
|
gptkb:television_channel
|
|
gptkbp:allows
|
training instability
requires large datasets
can produce artifacts
|
|
gptkbp:applies_to
|
unpaired datasets
|
|
gptkbp:can_be
|
day to night
horses to zebras
monet paintings to photographs
summer to winter
|
|
gptkbp:developed_by
|
gptkb:Eli_Shechtman
gptkb:Jun-Yan_Zhu
gptkb:Phillip_Isola
gptkb:Taesung_Park
|
|
gptkbp:has_programs
|
gptkb:musician
augmented reality
art generation
photo enhancement
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Cycle GAN
|
|
gptkbp:is_compared_to
|
gptkb:Star_GAN
gptkb:pix2pix
gptkb:WGAN
gptkb:Style_GAN
|
|
gptkbp:is_discussed_in
|
workshops
conferences
online forums
seminars
|
|
gptkbp:is_evaluated_by
|
user studies
quantitative metrics
|
|
gptkbp:is_explored_in
|
gptkb:Artificial_Intelligence
gptkb:software_framework
gptkb:technology
image processing
|
|
gptkbp:is_implemented_in
|
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch
|
|
gptkbp:is_influenced_by
|
gptkb:pix2pix
Cycle-consistent loss
GA Ns
|
|
gptkbp:is_part_of
|
computer vision
deep learning
|
|
gptkbp:is_recognized_by
|
gptkb:DJ
gptkb:Stanford_University
gptkb:Google_Research
gptkb:MIT_Media_Lab
|
|
gptkbp:is_related_to
|
domain adaptation
image synthesis
style transfer
|
|
gptkbp:is_supported_by
|
gptkb:Research_Institute
academic papers
open-source implementations
|
|
gptkbp:is_used_for
|
image-to-image translation
|
|
gptkbp:is_used_in
|
film production
research projects
art installations
commercial applications
|
|
gptkbp:published_by
|
gptkb:2017
|
|
gptkbp:uses
|
cycle consistency loss
two GA Ns
|
|
gptkbp:bfsParent
|
gptkb:television_channel
|
|
gptkbp:bfsLayer
|
3
|