Is WGAN better than DCGAN?

Is WGAN better than DCGAN?

WGAN result in stable training and solves the problem of mode collapse and vanishing gradient which we face in DCGAN. But all this comes with a price i.e WGAN is slower than DCGAN while training.

Why was Wasserstein Gan used?

The Wasserstein Generative Adversarial Network, or Wasserstein GAN, is an extension to the generative adversarial network that both improves the stability when training the model and provides a loss function that correlates with the quality of generated images.

How do you train Wgan GP?

To train a WGAN-GP model, you must train the discriminator for more iterations than the generator. In other words, for each generator iteration, you must train the discriminator for multiple iterations. Train with a mini-batch size of 64 for 10,000 generator iterations.

What is DC Gan?

DCGAN is one of the popular and successful network design for GAN. It mainly composes of convolution layers without max pooling or fully connected layers. It uses convolutional stride and transposed convolution for the downsampling and the upsampling. The figure below is the network design for the generator.

What is the gradient penalty?

A Gradient Penalty is a soft version of the Lipschitz constraint, which follows from the fact that functions are 1-Lipschitz iff the gradients are of norm at most 1 everywhere. The squared difference from norm 1 is used as the gradient penalty.

How do you start GAN?

The fundamental steps to train a GAN can be described as following:

1. Sample a noise set and a real-data set, each with size m.
2. Train the Discriminator on this data.
3. Sample a different noise subset with size m.
4. Train the Generator on this data.
5. Repeat from Step 1.

Where can I find the code for dcgan?

I have used the TensorFlow’s official tutorial code of DCGAN as the foundation code for this tutorial and modified it for WGAN. You can find it here. DCGAN consists of two neural networks like every other GAN i.e one Generator and one Discriminator. The generator takes random noise as input and outputs a generated fake image.

Which is better discriminator or generator in dcgan?

And discriminator’s goal is w-loss with a weighted penalty. While training WGAN, the discriminator is trained multiple times for each step, while the generator is trained once per step. WGAN result in stable training and solves the problem of mode collapse and vanishing gradient which we face in DCGAN.

Which is better, WGAN or dcgan for mode collapse?

While training WGAN, the discriminator is trained multiple times for each step, while the generator is trained once per step. WGAN result in stable training and solves the problem of mode collapse and vanishing gradient which we face in DCGAN. But all this comes with a price i.e WGAN is slower than DCGAN while training.

Which is a better Gan, WGAN or lsgan?

I did not end up spending much time on WGAN and DCGAN, because I decided to try an even more recent GAN implementation called the LSGAN (Least-Squares GAN). It turned out to produce better results compared to the other 2 implementations.