Variational Autoencoder (VAE) Uses and Benefits

Key takeaways

Variational autoencoders (VAE) are machine learning models you can use to generate new data, process data from signals, detect anomalies, and more. At a glance, here's what you need to know:

• VAEs are useful for anomaly detection, image processing, and dimensionality reduction. 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. 

• Other forms of autoencoders include adversarial autoencoders and sparse autoencoders, but each handles latent space differently.

Learn more about VAEs and how they work, as well as careers that use machine learning models in their day-to-day roles. If you’re interested in learning more about advanced machine learning techniques, Deep Learning Specialization from DeepLearning.AI can help you learn to build and train neural networks, optimize algorithms, and master theoretical concepts.

What is a Variational Autoencoder (VAE)? 

A variational autoencoder (VAE) can generate variations of data, including images and text. They combine probabilistic techniques with traditional autoencoding to give you tools for data generation, anomaly detection, and dimensionality reduction. Unlike traditional autoencoders, VAEs focus on learning a probabilistic distribution of data, enabling you to generate new samples consistent with the original data set or variations.

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. 

From image synthesis to health care applications, VAEs have become one of the driving forces pushing the boundaries of artificial intelligence today.

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. 

What are variational autoencoders used for?

VAEs are widely used, helping you generate data, detect anomalies, reduce dimensionality, and process images and videos. Some of the different applications of VAEs include:

1. Data generation

You can use variational autoencoding to generate realistic data samples. By sampling from the latent space, VAEs can create entirely new data points resembling the original data set. This might come in handy during tasks like data augmentation, for example, which you can use to improve machine learning model performance.

2. Anomaly detection

Variational autoencoders can learn the normal distribution of data and identify anomalies or outliers that deviate from that norm. This can be helpful when looking for fraudulent credit card transactions; deviations from typical spending behavior would trigger a red flag.

3. Dimensionality reduction

VAEs compress high-dimensional data into more meaningful latent space representations. This simplifies analysis and visualization. In health care settings, for instance, you can use VAE to create high-quality 3D medical scans or images.

3, Image and video processing

Variational autoencoders can also enhance and generate visual content. VAEs can remove noise from low-quality images, convert low-quality images into high-resolution versions, and even create video sequences by predicting future frames based on temporal relationships in data.

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.

Challenges and future directions

As with any emerging AI technology, VAEs are set to become more advanced and will likely face new hurdles in the coming years. Consider the following potential challenges related to model complexity, mode collapse, and interpretability.

1. Model complexity

Balancing complexity and performance remains a critical challenge for VAEs. Overly complex models risk overfitting, while overly simple models might lack expressiveness. Moving forward, VAEs will likely aim to strike a consistent balance between a high-performing model with just the right amount of complexity.

2. Model collapse

This common issue in generative AI models occurs when a model generates limited diversity in its samples. To address it, you need advanced regularization techniques and hybrid approaches that combine VAEs with other generative models (such as convolutional or recurrent neural networks).

3. Interpretability

Understanding and interpreting the latent space remains a challenge for VAEs. Going forward, expect researchers to focus on developing more interpretable latent representations, allowing better insights into the underlying data structures.

Read more: Exploring Deep Learning Frameworks: Tools for Building Intelligent Systems

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

Total median pay in the US (Glassdoor): $156,000 [1]

Job outlook (projected growth from 2024 to 2034): 34 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

Total median pay in the US (Glassdoor): $167,000 [3]

Job outlook (projected growth from 2024 to 2034): 34 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

Total median pay in the US (Glassdoor): $114,000 [4]

Job outlook (projected growth from 2024 to 2034): 15 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

Total median pay in the US (Glassdoor): $162,000 [6]

Job outlook (projected growth from 2024 to 2034): 20 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

Subscribe to our weekly LinkedIn newsletter, Career Chat, where you can stay up to date with industry developments, career tips, and skill-building resources. Then, check out some of our other free resources to learn more about artificial intelligence:

• Watch on YouTube: Artificial General Intelligence vs. AI: How Close Are We?

• Take the quiz: AI Career Quiz: Is It Right for You? Find Your Role

• Hear from experts: Bots & Blueprints: 6 Questions with a Software Architect and AI Developer

Whether you want to develop a new skill or get comfortable with an in-demand technology, you can keep growing with a Coursera Plus subscription. You’ll get access to over 10,000 flexible courses.

Build job-ready skills with Coursera Plus

Start 7-day free trial

• 
• 
• 
• 

Start 7-day free trial