What Are Conditional Generative Adversarial Networks (cGANs)?

Conditional Generative Adversarial Networks (cGANs), which extend the capabilities of Generative Adversarial Networks (GANs), are groundbreaking in the field of artificial intelligence when it comes to generating synthetic data that mimics actual data. It has uses across various industries, including image generation, creating training data, and completing missing data in a set. Additionally, cGANs represent a critical breakthrough that makes incorporating machine learning and artificial intelligence into businesses more feasible.

Discover what cGANs are, the different types you can use, and how you might see this machine learning method used in everyday applications across fields.

What are cGANs?

Generative Adversarial Networks (GANs) are a type of artificial neural network designed in 2014 by an American computer scientist named Ian Goodfellow [1]. This model is commonly used in machine learning for image enhancement, image classification, and computer vision applications.

A GAN consists of two neural networks: the generator and the discriminator. In this model, the generator produces data, and the discriminator evaluates it. The generator's goal is to create data that the discriminator can’t identify as fake. At the same time, the discriminator's goal is to decide whether data has been manufactured or not. The process continues until the generator produces the highest-quality synthetic data.

cGANs extend GAN functions by adding a condition to the data generation process. This condition could be anything from a label associated with an image to specific attributes the generated data should contain, allowing for a more precise data generation process. 

Types of cGAN applications

You can choose different types of cGANs designed to excel in various scenarios and applications. Some examples you might choose from include the following.

• Pix2Pix: This cGAN is designed for image-to-image translation tasks. It works by training on pairs of related images (like a sketch and its corresponding photograph) and learning to transform an input image from one style into another.

• CycleGAN: Unlike Pix2Pix, CycleGAN can perform image translations without paired examples. This makes it ideal for tasks where collecting paired datasets is challenging, such as transforming horses into zebras.

• Super-resolution GAN (SRGAN): SRGAN focuses on enhancing the resolution of images. It takes low-resolution inputs and generates high-resolution outputs, making it useful in applications like improving old picture quality.

• StyleGAN: Developed by NVIDIA, StyleGAN generates highly realistic and customizable images, such as faces that don’t exist in real life. This cGAN can create novel faces or add features to existing faces like freckles or a different hairstyle.

What are cGANs used for?

cGANs use labels to inform data generation, making it easier for you to direct the type of data produced. For example, if the condition is a label indicating “dog,” the generator will create images of dogs, and the discriminator will evaluate them based on whether the image seems like it is real. You can use this process for several applications, such as:

• Image-to-image translation: cGANs can transform one type of image into another, like turning sketches into colorful pictures.

• Super-resolution: cGANs can take low-resolution images and enhance them, producing images with high-definition.

• Style transfer: cGANs can take the style of one image, like the signatures of a famous painter’s style, and apply it to another image.

• Data augmentation: cGANs can generate additional, varied data based on existing samples, boosting datasets for training machine learning models.

Who uses cGANs?

Various industries, including digital entertainment, finance, and health care, benefit from cGANs thanks to their unique ability to generate conditioned data. In general, developing cGANs requires a strong background in computer programming and machine learning, so you’re likely to need a background in these areas to be on the creating side of this technology. However, you might use cGANs and benefit from their technology in several other professions, specifically those benefiting from image processing and enhancement.

For example, as a health care provider, you might use cGANs to enhance medical images and aid in diagnostics. cGANs can help to improve the image quality of MRIs and ultrasound images, including segmenting images, which improves diagnostic accuracy in cases such as tumor identification. While medical applications are typical, you might also use the image-enhancing properties of cGANs in other ways. In public safety and construction, professionals can use cGANs to enhance road monitoring images, helping identify areas needing urgent attention.

Pros and cons of using cGANs

One of the primary benefits of cGANs is the ability to have more control over the data generated by applications, which helps unlock the potential for using these networks. While not comprehensive, some of the advantages and limitations of this method you might experience are as follows.

Pros

• Controlled data generation: cGANs allow for data generation that meets specific conditions, allowing you to give details related to the type of output you are looking for.

• Enhances capabilities of standard GANs: By introducing conditions, cGANs expand the capabilities of GANs, offering more precision and relevance in the data generated.

Cons

• Difficulty with training: If the model design is incorrect, the algorithm outputs can be unstable or inaccurate.

• Complexity: To effectively learn and generate conditioned data, cGANs have an added complexity compared to GANs and other machine learning models, which can make them more time and computationally intensive.

How to start learning cGANs

cGANs are an advanced topic in machine learning, so strengthening your basic knowledge can be a good strategy to build toward this type of function. Before tackling cGANs, ensure you have a good grasp of neural networks and their principles. A deep understanding of GANs is a good starting place, as cGANs build upon this foundational concept by introducing conditions to the data generation process.

Reading up on the latest research can also help you learn about the development and application of cGANs. Start with the seminal papers on GANs and cGANs to understand their evolution and critical concepts. Websites like PubMed and Google Scholar are excellent resources for finding these papers. To extend into coursework, consider online platforms like Coursera to learn from industry leaders and gain hands-on practice.

Get started with Coursera.

Now that you know more about conditional generative adversarial networks, consider continuing to learn about machine learning topics with courses on Coursera designed for learners at every skill level. For example, you can get a comprehensive overview with the Machine Learning Specialization offered in tandem by DeepLearning.AI and Stanford University. This Specialization is a three-course series focusing on supervised and unsupervised machine learning and how to apply these methods in real-world professions.