티스토리 뷰
09.18.2024
Gradient Penalty라고도 불리나 봄.
Applying a gradient penalty in GAN training (especially in WGAN-GP) helps stabilize the training process by enforcing the Lipschitz continuity condition. This regularizes the discriminator’s gradients, ensuring that they remain well-behaved, which in turn helps the generator receive more meaningful updates. It addresses critical issues like mode collapse, vanishing/exploding gradients, and the problems with weight clipping in GANs.
글보다 해석이 훨씬 직관적이네. 3D-aware GAN 읽다보니 자주 표현하는 텀이었음.
Can someone explain R1 regularization function in simple terms?
I'm trying to understand the R1 regularization function, both the abstract concept and every symbol in the formula. According to the article, the definition of R1 is: It penalizes the discriminato...
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