VAE vs. GAN: What’s the Difference?

Key takeaways

While a VAE compresses data to its most important features before reconstructing a new piece of data, a GAN uses a gamified process.

• Three applications of VAEs include signal analysis, content generation, and medical research.

• While GANs are better for generating multimedia, VAEs are more effective for signal analysis.

• You can use GANs to generate content such as images, videos, and text.

Explore VAEs versus GANs, including how they work, their strengths and weaknesses, and how to use them. If you’re ready to enhance your GenAI skill set, enroll in the Generative AI Fundamentals Specialization from IBM. In as little as four weeks, you can learn about AI product strategy, data ethics, responsible AI, prompt patterns, and more.

VAE vs. GAN

Variational autoencoders (VAEs) and generative adversarial networks (GANs) are both artificial intelligence models you can use to create content like images, videos, or text. They have some similarities, such as an architecture that requires two neural networks to work together to make the final output. However, their approach to generating novel data differs, meaning they work differently and are helpful in different situations. 

In simple terms, a variational autoencoder compresses 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 in which one AI model creates an output similar to training data, and the other model attempts to spot the fake.

Explore 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 variational inference to compress or encode data before accurately reconstructing or decoding it, retaining all of the most important features (variables). The generative model uses two neural networks, the encoder and the decoder, to accomplish this task. 

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 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 while representing novel 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 flexibly define patterns and efficiently represent the input. 

Read more: What Is a Hidden Layer in a Neural Network?

How does a variational autoencoder work? 

A variational autoencoder contains two neural networks: an encoder and a decoder. The input first goes to the encoder, which identifies the data's latent variables. 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 calculates 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. 

The compressed data then arrives at the bottleneck, which acts as the encoder's last layer and the decoder's first layer. The decoder uses Gaussian noise, or the 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 data streams, map trends, and identify patterns. You could use this technology in many different industries, such as monitoring stock market patterns or health care monitoring. 

• Generating content: VAEs can 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: VAEs can help scientists 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 from a VAE. Instead of encoding and decoding the input, a GAN consists of dueling neural networks that work against each other to create a novel image using training data, a generator, and a discriminator.

These two neural networks play different roles: The generator creates fake content, and the discriminator spots the phony content. You can use many specialized GAN networks, such as a conditional GAN. This allows you to add conditions for the novel content the GAN produces, or a deep convolutional GAN, 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 AI-generated 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 can “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 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 create synthetic sounds. 

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

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

VAE vs. GAN: Which is better? 

You can generate new content using a variational autoencoder and a generative adversarial network. However, as both models approach the problem differently, they excel at different tasks. Generally, a GAN is better for generating multimedia like images, sounds, voices, and videos. You could also use a GAN model to develop 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: signal analysis. VAE’s ability to create an output that is mathematically accurate to an input means that you can use this technology to monitor data streams to detect anomalies and make predictions about what will happen. 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. 

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

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