Understanding differences b/w DCGAN and WGAN. implementing WGAN with TensorFlow 2.x. In this article, we will be trying to understand the difference b/w two basic types of GAN i.e DCGAN and WGAN, and will also be looking at the difference b/w them and the implementation of WGAN with TensorFlow 2.x.
In this article, we will be trying to understand the difference b/w two basic types of GAN i.e DCGAN and WGAN, and will also be looking at the difference b/w them and the implementation of WGAN with TensorFlow 2.x. 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.
Some issues that arise in DCGAN are due to the use of Binary Cross-Entropy loss and are as follows.
One Solution for the issues discussed above is to use Wasserstein loss that approximates Earth Mover’s Distance (EMD is the amount of effort needed to make one distribution to another distribution. In our case we want to make the generated image distribution equal to the real image distribution). WGAN makes use of Wasserstein loss, so let us now talk about WGAN.
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