R1 Regularization
R1 Regularization
**R_INLINE_MATH_1 Regularization** is a regularization technique and gradient penalty for training [generative adversarial networks](https://paperswithcode.com/methods/category/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](https://paperswithcode.com/method/gan) game.

This leads to the following regularization term:

$$ R\_{1}\left(\psi\right) = \frac{\gamma}{2}E\_{p\_{D}\left(x\right)}\left[||\nabla{D\_{\psi}\left(x\right)}||^{2}\right] $$
Which Training Methods for GANs do actually Converge?
https://paperswithcode.com/paper/which-training-methods-for-gans-do-actually
Which Training Methods for GANs do actually Converge?