What is GAN image generation?

What is GAN image generation?

Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images.

What is a VAE-GAN?

VAE-GAN stands for Variational Autoencoder- Generative Adversarial Network (that is one heck of a name.) Before we get started, I must confess that I am no expert in this subject matter (I don’t have PhD in electrical engineering, just sayin’).

What is the difference between GAN and conditional GAN?

In GAN, there is no control over modes of the data to be generated. The conditional GAN changes that by adding the label y as an additional parameter to the generator and hopes that the corresponding images are generated. We also add the labels to the discriminator input to distinguish real images better.

How is a Gan different from a VAE?

In VAE, we optimize the lower variational bound whereas in GAN, there is no such assumption. In fact, GANs don’t deal with any explicit probability density estimation. The failure of VAE in generating sharp images implies that the model is not able to learn the true posterior distribution.

How are VAE and Gan used in text to image generation?

Attributed to such stacked VAE-GAN structure, two kinds of generative models can boost each other for more effective and stable text-to-image generation. Experimental results on 2 widely-used datasets empirically verify the effectiveness of our proposed approach.

How is conditional GAN used in text to image generation?

Then, conditional GAN is adopted for refining the generation of VAE, which recovers lost details and corrects the defects for realistic image generation. Attributed to such stacked VAE-GAN structure, two kinds of generative models can boost each other for more effective and stable text-to-image generation.

Which is better VAE or an inference model?

The best thing of VAE is that it learns both the generative model and an inference model. Although both VAE and GANs are very exciting approaches to learn the underlying data distribution using unsupervised learning but GANs yield better results as compared to VAE.