GAN Compression: Efficient Architectures for Interactive Conditional GANs

Conditional Generative Adversarial Networks (cGANs) have enabled controllable image synthesis for many computer vision and graphics applications. However, recent cGANs are 1-2 orders of magnitude more computationally-intensive than modern recognition CNNs... (read more)

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Methods used in the Paper


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