Topmusicanswers
How does GAN loss work?
The GAN using Wasserstein loss involves changing the notion of the discriminator into a critic that is updated more often (e.g. five times more often) than the generator model. The critic scores images with a real value instead of predicting a probability.
Do GAN loss functions really matter?
Our analysis shows that loss functions are only successful if they are degenerated to almost linear ones. We also show that loss functions perform poorly if they are not degenerated and that a wide range of functions can be used as loss function as long as they are sufficiently degenerated by regularization.
What is GAN method?
A generative adversarial network (GAN) is a machine learning (ML) model in which two neural networks compete with each other to become more accurate in their predictions. GANs typically run unsupervised and use a cooperative zero-sum game framework to learn.
Why is GAN unstable?
The fact that GANs are composed by two networks, and each one of them has its loss function, results in the fact that GANs are inherently unstable- diving a bit deeper into the problem, the Generator (G) loss can lead to the GAN instability, which can be the cause of the gradient vanishing problem when the ...
