Mix and match networks: encoder-decoder alignment for zero-pair image translation

We address the problem of image translation between domains or modalities for which no direct paired data is available (i.e. zero-pair translation). We propose mix and match networks, based on multiple encoders and decoders aligned in such a way that other encoder-decoder pairs can be composed at test time to perform unseen image translation tasks between domains or modalities for which explicit paired samples were not seen during training... (read more)

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Methods used in the Paper


METHOD TYPE
Colorization
Self-Supervised Learning
Residual Connection
Skip Connections
GAN Least Squares Loss
Loss Functions
Cycle Consistency Loss
Loss Functions
Tanh Activation
Activation Functions
Residual Block
Skip Connection Blocks
Instance Normalization
Normalization
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