What Is a Conditional Generative Adversarial Network?

Written by Coursera Staff • Updated on

Learn how a conditional generative adversarial network works, how it’s different from traditional GANs and DCGANs, and how AI engineers and scientists are using cGANs to tackle real-world issues.

[Featured Image] A data scientist works on a laptop to develop a conditional generative adversarial network (cGAN).

A conditional generative adversarial network (cGAN) is a type of neural network that uses labels—or conditions—to generate novel text or images that have characteristics similar to its training data set. The difference between a typical generative adversarial network (GAN) and a conditional GAN is that you use labeled data to provide context to a conditional GAN, allowing you to get better, more targeted results from the generator. 

In this article, you will learn more about conditional generative adversarial networks, how they work, and how to use them. 

What is a generative adversarial network?

A generative adversarial network (GAN) is a combination of two neural networks working together to create a novel output that mimics training materials. For example, you can train a generative adversarial network with images of human faces and ask it to create a brand new human face similar but not the same as the examples you provided. Understanding how GANs work can help you understand how a conditional generative adversarial network works. 

How does a generative adversarial neural network work?

The two neural networks inside a GAN each have a role and a title: the generator and the discriminator. The two AI minds play a game with each other to respond to a query. The generator’s job is to create a fake output that is so convincing that the discriminator can’t distinguish the real from the forgery. Meanwhile, the discriminator is attempting to spot the fake without being tricked. 

Each neural network wants to win the game, which means that both sides are motivated to continually improve their game. The generator creates a version of the output, but the discriminator can determine it is a fake. So, the generator will attempt to create an even better forgery. The game will go back and forth until the generator is successful and the discriminator loses the game. The results of that game become the output of the neural network. 

Read more: 8 Common Types of Neural Networks

What is a conditional generative adversarial network?

With a conditional generative adversarial network, you provide the neural network context and guidelines for what kind of answer or output to produce. Instead of reaching out into random noise to provide an answer, a cGAN gives the network a condition or specific information about what kind of answer to produce. This makes it easier to get the specific sort of results you’re looking for instead of something a little more random. For example, you can ask for a certain labeled kind of data, like a specific digit or the specifications of the image you want the network to create. 

One benefit of a conditional generative adversarial network is that you can give the network context so that it responds appropriately in different situations. Another benefit is that you can explore generative possibilities for limited situations. For example, you could direct a generative adversarial network to fill in an empty lot with property examples that would fit the neighborhood and lot size. 

What is the difference between a cGAN and a DCGAN?

Although these two acronyms are very similar and the processes are based on the GAN structure, their functions and features are different. DCGAN stands for deep convolutional generative adversarial network. These are GANs with a convolutional layer included in both the generator and discriminator sides. The convolutional layer adds a more nuanced understanding of the images involved in generating the request, which can lead to a better-quality output. 

Unlike cGAN, DCGAN does not have conditions by default, but you can create a conditional DCGAN to combine both ideas into one model. DCGANs are most commonly used to generate high-quality images. 

What is a conditional generative adversarial network used for? 

Conditional generative adversarial networks are tools that scientists and analysts use in many different industries. They play an important role in AI technology, such as: 

  • Computer vision: cGANs are an important tool for AI vision, or the ability of a computer to understand and react according to visual input.  

  • Image generation: Some image generators are powered by cGANs. This technology can create new images from text or image-based prompts. 

  • Image to font style transformation: cGANs can translate text or font into different styles. You could also use a conditional generative adversarial network to extrapolate an entire alphabet in a certain font style based on one or two submitted words. 

Beyond these common applications, cGANs are flexible tools you can adapt to highly specific uses. Let’s examine a range of ways people use cGANs to solve complex problems. 

  • Simulating how fuel sprays out of aero engine combustors: Scientists can use cGANs to simulate how fuel sprays and splatters while injected into the pressurized combustion chamber inside an aircraft. This is important because getting the right fuel injection can mean a cleaner and more environmentally friendly flight. A conditional generative adversarial network can model an optimized approach, saving time and resources that might have been spent on manual spray analysis. 

  • Classifying urban areas for land use: Urban planners can use cGANs to sort hyperspectral imaging and light detection ranging (lidar) data to create land use/cover classification maps. These maps help developers and planning boards visualize how the community uses the land or plans to use the land in the future. Traditional methods of creating these maps require professionals to sit and study maps manually. A conditional generative adversarial network can process the data much faster. 

  • Estimating the effects of medical treatments: When medical professionals decide which treatments to use for their patients, they rely on data about how the average patient responds to said treatment. Using conditional GANs, medical professionals can access more accurate data by tailoring the results to the patient’s biomarkers. This allows for more precise treatment in medical fields like oncology, where patient variables make a big difference in treatment outcomes. 

  • Designing and optimizing aircraft: When aerospace engineers design aircraft, they rely on computational fluid dynamic simulations to model how their build will react under different conditions. These resource-heavy simulations are preferable to physical testing because they eliminate the need to build a prototype before testing. However, engineers can improve the design process even further by using cGANs to predict flow under particular circumstances. 

Who uses conditional generative adversarial networks? 

To begin a career working with conditional generative adversarial networks, a bachelor’s degree in computer science or a related field can help you qualify for several types of jobs. You could be qualified for three potential careers with this education: generative AI engineer, generative AI data scientist, and AI researcher. 

1. Generative AI engineer

Average annual salary (US): $127,810 [1]

Job outlook (projected growth from 2022 to 2032): 25 percent [2]

Education requirements: The most common degree for a generative AI engineer in the United States is a bachelor’s degree, typically in computer science or a math-related field, such as statistics. You might also need a master's degree. 

As a generative AI engineer, you will help to build generative artificial intelligence systems to solve the problems of your industry or company. Depending on your industry, you might spend time looking for or designing new AI tools, setting up or designing infrastructure, and determining the correct data sets for training. You may work with a team of other AI engineers. 

2. Generative AI data scientist

Average annual salary (US): $129,954 [3]

Job outlook (projected growth from 2022 to 2032): 35 percent [4]

Education requirements: Data scientists typically obtain a bachelor’s degree in computer science, statistics, or mathematics. 

As a generative AI data scientist, you will use data to conduct research relevant to your industry or company's challenges. Responsibilities might include leading generative artificial intelligence initiatives, educating teams about generative AI tools and best practices, and developing company policies for how to work with generative AI. In this role, you will also be responsible for communicating with stakeholders about the generative AI projects you work on. 

Read more: What Is a Data Scientist? Salary, Skills, and How to Become One

3. AI researcher 

Average annual salary (US): $108,987 [5]

Job outlook (projected growth from 2022 to 2032): 23 percent [6]

Education requirements: To become a research scientist, you typically need a bachelor’s degree, although a master’s or doctorate is common. 

As an AI research scientist, your work will help find solutions for complex problems regarding AI. You may help develop new AI technologies, test new algorithms and AI techniques, develop new algorithms, and publish your findings in scientific journals. In this role, you will likely work with a team of other researchers. 

Read more: 6 Artificial Intelligence (AI) Jobs to Consider

Learn more with Coursera. 

If you’re ready to take the next step in a career with generative adversarial networks or just want to learn more about GANs, consider earning your Generative Adversarial Networks (GANs) Specialization offered by Deep Learning.AI on Coursera. This three-course series will help you learn more about GANs, including the discriminator, the generator, image-to-image translation, and more.  

Article sources


Glassdoor. “Salary: AI Engineer in the United States, https://www.glassdoor.com/Salaries/ai-engineer-salary-SRCH_KO0,11.htm.” Accessed March 28, 2024.

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