Variational Autoencoder (VAE) Uses and Benefits

Variational autoencoders (VAE) are machine learning models you can use to generate new data, process data from signals, detect anomalies, and more. They work by compressing data down to its most fundamental components before rebuilding it with average values, creating something that looks unique but similar to the original data. You can use different variations of VAEs for different purposes, and professionals like data scientists, machine learning researchers, machine learning engineers, and health care analysts use VAEs. 

Learn more about VAEs and how they work, as well as careers that use machine learning models in their day-to-day roles. 

What is a Variational Autoencoder (VAE)? 

A variational autoencoder (VAE) can generate variations of data, including images and text. This type of machine learning model can create data similar to the data it saw while training by breaking down complex entities into the most essential components and then rebuilding the data with random samples from the breadth of training data. This allows a VAE to generate data that looks unique but resembles other similar data. 

How does a variational autoencoder work?

All autoencoders are neural networks with three parts: an encoder, a decoder, and a representation of latent space. The input data goes first to the encoder, which compresses the data down to its fundamental components. These fundamental components are the data's defining features, and they are collectively known as latent space. Next, the decoder rebuilds the data from latent space. 

It differs from a traditional autoencoder in the way it encodes and decodes data. Traditional autoencoder models encode a fixed representation of latent variables, but a variational autoencoder encodes latent variables based on probability. A VAE also uses probability to rebuild the data by adding white noise, or Gaussian noise, to recreate what the pixels of the image most likely are. 

For example, if you trained a VAE using images of dogs, the model would encode each image to latent space and learn the pattern within the images. Those simplified variables lose most of the details in an image but retain the information the model needs to recreate the image. When you ask the model to generate images of a dog, it starts with the latent space it encoded during training while analyzing images of dogs, then adds Gaussian noise to guess what the other pixels would look like. 

Types of VAEs

You can use many different kinds of VAE architecture to modify the uses of your model and to overcome common challenges associated with VAEs. Two examples of VAE variations include VAE-GANs and conditional VAEs. 

VAE-GAN 

A variational autoencoder-generative adversarial network (VAE-GAN) is a hybrid neural network model that combines the best features of a VAE and a GAN to generate better results. 

A GAN is a type of neural network that also uses two components to generate data that looks unique: a generator and a discriminator. The two neural networks play a game during which they each train based on the other’s mistakes. The generator’s goal is to create a fake entity that could pass as training data, and the discriminator’s goal is to learn the difference between real and fake entities and correctly identify the generator’s fakes. The two play back and forth until the generator wins, and the discriminator can’t tell the fake (generated) data from training data. 

A VAE-GAN combines the two models, allowing you to overcome some of the problems you might find with either model. For example, GANs are skilled at creating realistic images thanks to the discriminator network. However, GANs require more training and computer power than a VAE, and VAEs can be a more secure choice. By combining the two models, you can get more accurate, higher-resolution images with less time and model training. 

Conditional VAEs 

A conditional VAE allows you to add constraints or conditions to how the model generates data. For example, instead of asking a conditional VAE to generate an image of a dog, you could use a conditional VAE to generate images of brown dogs, poodles, or dogs running. This allows you to use a VAE trained on general information to produce results you can use for a specific purpose instead of creating and training a model from scratch. It increases the control you have over what kind of output the model provides.

What is a VAE used for? Applications of VAEs

You can use a VAE for many tasks, including image or audio generation, signal processing, and anomaly detection. Each of these applications can be used in a wide range of industries and use cases: 

• Data generation: Variational autoencoders can generate data like images, audio, and more. Generative AI has use cases in diverse industries such as retail, logistics, health care and medical research, marketing, business, and more. 

• Signal processing: You can use a variational autoencoder for signal processing, which involves extracting data from complex signals. For example, you can use a VAE to understand the volatility of investments or differentiate between different speakers in an audio recording. 

• Anomaly detection: VAEs can provide anomaly detection by learning what normal patterns look like and detecting patterns that don’t fit that norm. You could use this in different industries, such as detecting financial fraud or when manufacturing equipment starts to malfunction. 

Who uses VAEs?

With applications in many different industries, VAEs are a machine learning model used by professionals like data scientists, machine learning research scientists, health care analysts, and machine learning engineers. Explore these roles, along with their average salary and job outlook in the United States. 

Data scientist

Average annual salary in the US (Glassdoor): $118,222 [1]

Job outlook (projected growth from 2023 to 2033): 36 percent [2]

As a data scientist, you will use machine learning models like VAEs to manipulate data in a variety of ways. In this role, you will help companies and organizations find actionable insight in their data. You will determine what data you need, then collect, store, and analyze data to uncover patterns. 

Machine learning researcher

Average annual salary in the US (Glassdoor): $144,750 [3]

Job outlook (projected growth from 2023 to 2033): 36 percent [2]

As a machine learning researcher, you will study machine learning algorithms like VAEs. You will design and develop new machine learning algorithms and create solutions to problems using ML models. In this role, you will conduct research to develop machine learning models that advance ML technology as a whole. 

Health care analyst

Average annual salary in the US (Glassdoor): $88,940 [4]

Job outlook (projected growth from 2023 to 2033): 16 percent [5]

As a health care analyst, you will work with health care-related data sets to find patterns and make recommendations to health care professionals to improve processes or patient outcomes. You may work with data such as emerging health care trends to help health care professionals stay informed of the latest developments, or you may work on projects such as increasing operational efficiency. 

Machine learning engineer

Average annual salary in the US (Glassdoor): $122,957 [6]

Job outlook (projected growth from 2023 to 2033): 26 percent [7]

As a machine learning engineer, you will use machine learning models like VAEs to create solutions to problems. Similar to a machine learning researcher, you will need to think creatively to develop machine learning solutions. But unlike a machine learning researcher, you will often develop machine learning solutions for end users like companies or customers, as opposed to pushing machine learning technology to new heights. 

Learn more about VAEs with Coursera

VAEs are machine learning models that encode data into a standard distribution of latent variables before rebuilding the data with white noise to create a unique entity that looks like training data. To learn more about working with machine learning algorithms like variational autoencoders, you can find programs on Coursera to support your learning, no matter your current knowledge level. Consider earning a Professional Certificate, such as the IBM Machine Learning Professional Certificate or the Google Data Analytics Professional Certificate, to help you build job-ready skills. Or explore a Specialization like the Generative AI Fundamentals Specialization offered by IBM to gain an in-depth knowledge of generative AI.