Multi-task GANs for Semantic Segmentation and Depth Completion with Cycle Consistency

Semantic segmentation and depth completion are two challenging tasks in scene understanding, and they are widely used in robotics and autonomous driving. Although several works are proposed to jointly train these two tasks using some small modifications, like changing the last layer, the result of one task is not utilized to improve the performance of the other one despite that there are some similarities between these two tasks... (read more)

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Methods used in the Paper


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