Lipschitz Regularized CycleGAN for Improving Semantic Robustness in Unpaired Image-to-image Translation

For unpaired image-to-image translation tasks, GAN-based approaches are susceptible to semantic flipping, i.e., contents are not preserved consistently. We argue that this is due to (1) the difference in semantic statistics between source and target domains and (2) the learned generators being non-robust... (read more)

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Methods used in the Paper


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