CSGAN: Cyclic-Synthesized Generative Adversarial Networks for Image-to-Image Transformation

The primary motivation of Image-to-Image Transformation is to convert an image of one domain to another domain. Most of the research has been focused on the task of image transformation for a set of pre-defined domains... (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
Leaky ReLU
Activation Functions
Sigmoid Activation
Activation Functions
Dropout
Regularization
Pix2Pix
Generative Models
Convolution
Convolutions
GAN
Generative Models