R1 Regularization

Introduced by Mescheder et al. in Which Training Methods for GANs do actually Converge?

R_INLINE_MATH_1 Regularization is a regularization technique and gradient penalty for training generative adversarial networks. It penalizes the discriminator from deviating from the Nash Equilibrium via penalizing the gradient on real data alone: when the generator distribution produces the true data distribution and the discriminator is equal to 0 on the data manifold, the gradient penalty ensures that the discriminator cannot create a non-zero gradient orthogonal to the data manifold without suffering a loss in the GAN game.

This leads to the following regularization term:

$$ R_{1}\left(\psi\right) = \frac{\gamma}{2}E_{p_{D}\left(x\right)}\left[||\nabla{D_{\psi}\left(x\right)}||^{2}\right] $$

Source: Which Training Methods for GANs do actually Converge?


Paper Code Results Date Stars


Task Papers Share
Image Generation 112 17.31%
Disentanglement 43 6.65%
Image Manipulation 32 4.95%
Face Generation 27 4.17%
Face Recognition 19 2.94%
Image-to-Image Translation 18 2.78%
Face Swapping 17 2.63%
Super-Resolution 15 2.32%
Translation 14 2.16%


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