Emotional Semantics-Preserved and Feature-Aligned CycleGAN for Visual Emotion Adaptation

Thanks to large-scale labeled training data, deep neural networks (DNNs) have obtained remarkable success in many vision and multimedia tasks. However, because of the presence of domain shift, the learned knowledge of the well-trained DNNs cannot be well generalized to new domains or datasets that have few labels... (read more)

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Methods used in the Paper


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