Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping

CVPR 2019 • Huan Fu • Mingming Gong • Chaohui Wang • Kayhan Batmanghelich • Kun Zhang • DaCheng Tao

Unsupervised domain mapping aims to learn a function to translate domain X to Y by a function GXY in the absence of paired examples. Finding the optimal GXY without paired data is an ill-posed problem, so appropriate constraints are required to obtain reasonable solutions... (read more)

PDF Abstract CVPR 2019 PDF CVPR 2019 Abstract

Code

Add Remove Mark official

No code implementations yet. Submit your code now

Tasks

Add Remove

Results from the Paper

Edit

Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

METHOD	TYPE
Batch Normalization	Normalization
Residual Connection	Skip Connections
PatchGAN	Discriminators
ReLU	Activation Functions
Tanh Activation	Activation Functions
Residual Block	Skip Connection Blocks
Instance Normalization	Normalization
Convolution	Convolutions
Leaky ReLU	Activation Functions
Sigmoid Activation	Activation Functions
GAN Least Squares Loss	Loss Functions
Cycle Consistency Loss	Loss Functions
CycleGAN	Generative Models

METHOD

TYPE

Batch Normalization