The GAN Hinge Loss is a hinge loss based loss function for generative adversarial networks:
$$ L_{D} = \mathbb{E}_{\left(x, y\right)\sim{p}_{data}}\left[\min\left(0, 1 + D\left(x, y\right)\right)\right] \mathbb{E}_{z\sim{p_{z}}, y\sim{p_{data}}}\left[\min\left(0, 1  D\left(G\left(z\right), y\right)\right)\right] $$
$$ L_{G} = \mathbb{E}_{z\sim{p_{z}}, y\sim{p_{data}}}D\left(G\left(z\right), y\right) $$
Source: Geometric GANPaper  Code  Results  Date  Stars 

Task  Papers  Share 

Image Generation  44  17.46% 
Conditional Image Generation  17  6.75% 
Multiagent Reinforcement Learning  7  2.78% 
Test  7  2.78% 
Speech Synthesis  7  2.78% 
Translation  7  2.78% 
SuperResolution  6  2.38% 
Reinforcement Learning (RL)  6  2.38% 
ImagetoImage Translation  6  2.38% 
Component  Type 


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