Reversible GANs for Memory-efficient Image-to-Image Translation

The Pix2pix and CycleGAN losses have vastly improved the qualitative and quantitative visual quality of results in image-to-image translation tasks. We extend this framework by exploring approximately invertible architectures which are well suited to these losses... (read more)

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Methods used in the Paper


METHOD TYPE
Residual Connection
Skip Connections
Tanh Activation
Activation Functions
Residual Block
Skip Connection Blocks
Instance Normalization
Normalization
GAN Least Squares Loss
Loss Functions
Cycle Consistency Loss
Loss Functions
CycleGAN
Generative Models
Concatenated Skip Connection
Skip Connections
PatchGAN
Discriminators
ReLU
Activation Functions
Batch Normalization
Normalization
Convolution
Convolutions
Leaky ReLU
Activation Functions
Sigmoid Activation
Activation Functions
Dropout
Regularization
Pix2Pix
Generative Models