VAE vs. GAN: What’s the Difference?

Variational autoencoders (VAE) and generative adversarial networks (GAN) are both AI models capable of generating new, unique content that looks similar to but is fundamentally different from training data. Both networks use a system of dueling neural networks to accomplish this task, but they do so using very different approaches. 

First, learn about variational autoencoders, how they work, and how you can use them. Then, explore how generative adversarial networks work and what kinds of data you can use them to generate. Finally, compare the strengths and weaknesses of these two generative AI models. 

VAE vs. GAN

VAE and GAN are both artificial intelligence models you can use to create content like images, video, or text. They have some similarities, such as an architecture that requires two neural networks working together to create the final output. However, their approach to generating novel data is different, which means they work in different ways and are useful in different situations. 

In simple terms, a variational autoencoder is a method for compressing data down to its most important features before reconstructing a new piece of data that retains the main characteristics of the input while remaining unique. A generative adversarial network, on the other hand, is a gamified process where one AI model creates an output similar to training data, and the other model attempts to spot the fake. Explore each of these generative models, how they work, and the applications you can use them for. 

What is a variational autoencoder?

A variational autoencoder is a type of autoencoder that uses a technique called variational inference to compress or encode data before accurately reconstructing or decoding data, retaining all of the most important features (variables). To accomplish this task, the generative model uses two neural networks, aptly named the encoder and the decoder. 

All autoencoders use this dueling neural network architecture to compress and decompress data. The key feature that makes a variational autoencoder different from other types of autoencoders is that it uses variational inference; a machine-learning technique that uses optimization to create a complex probability field. The model uses this data to recreate a probabilistic approximation of the original content that retains the key variables but represents a novel piece of content. 

Other specialized autoencoders work with inputs in various ways. For example, a sparse autoencoder uses only a small percentage of its hidden layer neurons to interact with data. This allows the model to use the remaining neurons to be flexible in how it defines patterns and efficiently represents the input. 

How does a variational autoencoder work? 

A variational autoencoder contains two neural networks: an encoder and a decoder. The input goes to the encoder first, which identifies the latent variables of the data. Latent variables represent points of information that, while not directly observable, explain how the data distribution underlies its features. Next, the encoder within a VAE will calculate the mean and variance of the data using a statistical distribution. This allows the AI to compress the data into a lower-dimensional space, retaining the most meaningful information and removing noise. 

Then, the compressed data arrives at the bottleneck, which acts as the last layer of the encoder and the first layer of the decoder. The decoder uses Gaussian noise, or Gaussian distribution of the latent data, to reconstruct the data in a novel or unique way. 

Applications of VAE

You can use variational autoencoders in many different ways. A few examples include signal analysis, generating content, and medical research: 

Signal analysis: You can use VAEs to monitor streams of data to map trends and identify patterns. You could use this technology in a lot of different industries, such as monitoring stock market patterns or in health care monitoring. 

Generating content: You can use VAEs to create new images, videos, or text. You can even generate more complicated data, like handwritten text or 3D models created from 2D images. 

Biology and medical research: You can use VAEs to gain insights into the meaningful features of cells and other biological material, measuring differences and understanding their functions in new ways. 

What is a generative adversarial network?

A generative adversarial network (GAN) is also an AI model that generates novel content from an input, but it operates differently than a VAE. Instead of encoding and decoding the input, a GAN contains dueling neural networks that work against each other to create a novel image based on training data, a generator, and a discriminator.

These two neural networks play a different role: the generator creates fake content, and the discriminator spots the fake content. You can use many different types of specialized GAN networks, like a conditional GAN. This allows you to add conditions for the novel content the GAN produces, or a deep convolutional GAN, which is a specialized algorithm that allows for image processing. 

How does a generative adversarial network work?

After you provide a GAN model with a large amount of training data, the generator can create new content that looks similar to its training data yet represents a new or unique piece of content. The discriminator will attempt to spot the tell-tale signs of an AI-produced piece of content. The generator will try again, learning to produce better representations. The discriminator will continue to reject the generator's attempts, learning to become more accurate at spotting AI-generated content. This gamified process continues back and forth until the generator is able to “fool” the discriminator, which is to say that the generator produces a piece of content convincing enough that the discriminator can’t distinguish the fake content from the real training content data. This “winning answer” becomes the output. 

Applications of GAN

You can use a generative adversarial network to generate data for many different purposes. For example, you might generate synthetic sounds, training data for other AI models, or data to complement an incomplete data set: 

Generating content: GANs can generate novel content, from images, videos, and text to more complicated data like handwritten numbers and creating synthetic sounds. 

Generating data: You can also use a generative adversarial network to generate training data, which you can use with other deep learning AI models. 

Extrapolate from incomplete data: You can use a GAN to make estimates of what information a data set could contain if it were complete. 

VAE vs. GAN: Which is better? 

You can use both a variational autoencoder and a generative adversarial network to generate new content. However, as both models approach the problem differently, they excel at different tasks. Generally, a GAN is a better choice for generating multimedia like images, sounds, voices, and videos. You may also choose to use a GAN model for generating concepts, such as new ideas for medications, ideas for designing new products, or training data for other AI models. The gamified process of a GAN can make a more convincing and sharper generated image than a VAE model. 

At the same time, you can use VAE models for something that GANs are less effective at, that being signal analysis. VAE’s ability to create an output so mathematically accurate to an input means that you can use this technology to monitor streams of data to detect anomalies and make predictions about what will happen in the future. For example, you could use stock market data to train a VAE model to make real-time predictions and offer advice about the product’s volatility. VAEs are also skilled for other purposes, such as detecting anomalies in medical imaging, such as brain scans. 

To get the best of both worlds, you could consider using a hybrid VAE-GAN model. Combining the two helps overcome the challenges of using one model or the other, and allows you to lean to the strengths of both systems to perform additional functions. For example, your VAE-GAN model may be able to create a variety of mathematical possibilities and allow you to select from different options. 

Learn more about VAE vs. GAN on Coursera

Variational autoencoders and generative adversarial networks are both generative AI models that use two neural networks to accomplish their tasks. However, VAEs create an estimate based on a probability that resembles training data, while GANs are skilled at creating an output that is indistinguishable from training data. If you want to learn more about generative AI models, you can start today on Coursera. You might enroll in Build Better Generative Adversarial Networks (GANs) offered by Deep Leanring.AI as part of their Generative Adversarial Networks (GANs) Specialization. Or, you could begin Generative AI: Introduction and Applications offered by IBM as part of the Generative AI Fundamentals Specialization.