What is Variational generative adversarial network

Variational Generative Adversarial Network (VAE-GAN): How it Works and Its Applications

Generative Adversarial Networks (GANs) is an extremely popular class of deep neural networks that have been successful in generating new images, audio, and video data. In recent years, Variational Autoencoders (VAEs) have emerged as another family of powerful tools for generative modeling.

The combination of these two models has led to the creation of Variational Generative Adversarial Networks (VAE-GANs). In this article, we take a closer look at VAE-GANs, their functioning, and some of the use cases where they have been successfully deployed.

In essence, VAE-GANs are a combination of the traditional GANs and VAEs. In GANs, a generator neural network is trained to generate data, whereas in VAEs, the model is trained to create a compressed version of the input data. By combining these two architectures, VAE-GANs attempt to capture the best of both worlds.

VAE-GANs' key advantage is that they can generate high-quality images while retaining the ability to control the features of the generated images. This ability to manipulate the features of a generated image is particularly useful in a variety of different applications, including image-to-image translation and fashion design.

How VAE-GANs work?

The basic structure of VAE-GANs is quite similar to a typical GANs architecture. However, the encoder architecture in VAE-GANs is replaced by a VAE encoder, and this is where the biggest difference lies between the two models.

To get a better sense of how VAE-GANs work, let us illustrate the process using the example of generating shoes.

First, the VAE encoder is used to create a low-dimensional representation, or bottleneck, of the input image. The objective here is to create a compressed version of the input image. If the input image to the model is of size $28 \times 28$, then the output of the VAE encoder would typically be something like a $2 \times 2$ matrix.

The compressed vector is now passed to the generator network, which produces a synthetic version of the input image, in this case, a pair of shoes.

However, VAE-GANs take things one step further than traditional GANs. That is, rather than simply producing a synthetic image output, the model now aims to produce a synthetic image that features controllable characteristics. For instance, the model can be trained to generate a synthetic image of shoes that are black, white, or brown, or any other color.

Let us now look at the role of the discriminator network in VAE-GANs. Here, the discriminator network is trained to distinguish between real and fake images generated by the generator network. However, the discriminator's role is not just to evaluate whether the input image is real or fake. The discriminator network is also used to train the VAE encoder by giving feedback on how to improve the bottleneck vector's quality.

This process continues until the generator network produces high-quality synthetic images that can be controlled by changing their features and the encoder produces a compressed vector, which retains the critical features of the input image.

Applications of VAE-GANs

Image-to-image translation is one of the most popular use cases for VAE-GANs. Here, the model can be trained to transform an image of a shoe into an endpoint of different shoes. For example, the model can be trained to take a picture of a white sneaker and transform it into a brown sneaker.

Another application of VAE-GANs is in the field of fashion design. By training a VAE-GAN on a dataset of shoes, the model can be used to generate new shoe designs. Here, the designer can control the features of the generated shoe design, such as color, style, and material. This level of creative control can be invaluable for designers in the fashion industry.

VAE-GANs also have applications in speech generation. By training the model on a dataset of sound files, the network can be used to generate new sounds. The synthesized sounds can be controlled by adjusting the parameters of the model, such as the pitch, tone, and rhythm.

Further, VAE-GANs also have applications in the medical field. For instance, in 3D CT scans, the model can be used to generate accurate 3D models of organs, which the model can then use to control the features of a new organ. In medical imaging, VAE-GANs can be used to generate synthetic scans of different body parts, which can be used to develop new treatments.


In summary, Variational Generative Adversarial Networks offer a powerful tool that combines the strengths of the GANs and VAEs architectures. By providing controllability over the synthesized data, VAE-GANs has applications in a variety of domains, including fashion design, speech generation, and medical imaging. VAE-GANs is an active area of research that has the potential to make significant contributions to deep learning and generative modeling techniques in the future.