CDGAN: Cyclic Discriminative Generative Adversarial Networks for Image-to-Image Transformation

Image-to-image transformation is a kind of problem, where the input image from one visual representation is transformed into the output image of another visual representation. Since 2014, Generative Adversarial Networks (GANs) have facilitated a new direction to tackle this problem by introducing the generator and the discriminator networks in its architecture... (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
PatchGAN
Discriminators
ReLU
Activation Functions
Convolution
Convolutions
Leaky ReLU
Activation Functions
Sigmoid Activation
Activation Functions