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 |
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Task | Papers | Share |
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Image Generation | 44 | 17.96% |
Conditional Image Generation | 17 | 6.94% |
Multi-agent Reinforcement Learning | 7 | 2.86% |
Speech Synthesis | 7 | 2.86% |
Translation | 7 | 2.86% |
Super-Resolution | 6 | 2.45% |
Reinforcement Learning (RL) | 6 | 2.45% |
Image-to-Image Translation | 6 | 2.45% |
Decision Making | 5 | 2.04% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |