Bayesian Cycle-Consistent Generative Adversarial Networks via Marginalizing Latent Sampling

19 Nov 2018 • Haoran You • Yu Cheng • Tianheng Cheng • Chunliang Li • Pan Zhou

Recent techniques built on Generative Adversarial Networks (GANs), such as Cycle-Consistent GANs, are able to learn mappings among different domains built from unpaired datasets, through min-max optimization games between generators and discriminators. However, it remains challenging to stabilize the training process and thus cyclic models fall into mode collapse accompanied by the success of discriminator... (read more)

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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
Leaky ReLU	Activation Functions
Sigmoid Activation	Activation Functions
GAN Least Squares Loss	Loss Functions
Cycle Consistency Loss	Loss Functions
CycleGAN	Generative Models
Convolution	Convolutions
GAN	Generative Models

METHOD

TYPE

Batch Normalization