What Is GAN Machine Learning and What Are Its Benefits?
Updated December 12, 2022
Machine learning has become one of the most important tools in data science, and generative adversarial network (GAN) learning is one of the newest fields in machine learning. It has a variety of uses that involve text design, image design and product development. If you’re a data science professional, it may be helpful for you to understand GAN machine learning and its benefits.
In this article, we explore what GAN machine learning is and how it works, explain the different applications, describe its benefits and drawbacks and provide examples to help you understand how GAN machine learning can improve your data science operations.
What is GAN machine learning?
GANs are generative models, which means that they can create synthetic data points that mimic a set of training data. Professionals often use them to create realistic images, translate text into images, make images more detailed or translate images into different formats.
How does GAN machine learning work?
GAN architectures comprise two competing neural networks called the generator and the discriminator. A neural network is a type of machine learning in which a set of interconnected nodes process input and learn as they go along. At their core, neural networks are simply a system of trial and error. The two neural networks within a GAN architecture compete with one another and create their own training data to increase each other's ability. The generator and the discriminator each have their own purpose, which includes:
The first neural network is the generator, or the generative model. When creating a generator, engineers often first decide on the desired outcome. They then build a training data set, or a data set that they can use to create rules within the neural network, that imitates its distribution. They can then randomize the data and can input it into the generator.
The generative model takes randomized data and transforms it into data points that imitate the distribution of the test data. Once the generator gains basic competency, the data passes to the second model, where it is compared with the test data. The goal of the generative model is to create a data set that the discriminative model can't distinguish from the original.
The purpose of a discriminator is to classify incoming data. It analyzes features in the data instances to decide whether they belong to a certain category. Most discriminative algorithms only deal with binary choices, meaning that they give a yes or no answer to whether a data point fits into a certain category.
In a GAN model, the discriminative algorithm compares the test data with inputs from the generative algorithm to determine whether the new data belongs to the original data set. This competition between the two algorithms allows the generative model to mimic the training data more and more closely while the discriminator becomes better at distinguishing between new data and the original training data. This allows the model to teach itself to imitate data distribution, often with great precision.
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Example of using a GAN
Below is a simple example of how someone might use GAN machine learning:
John wants to create a program that can simulate human handwriting. To do this, he creates a data set that contains letters in many different styles of handwriting. Using this data set, he trains his generator neural network to produce machine-created letters that resemble handwritten ones. The goal of the generator neural network is to produce letters that can pass for authentic by the discriminator neural network, while the discriminator network wants to only identify authentic handwritten letters.
After creating a letter, the generator passes this letter, alongside real handwritten letters, to the discriminator. The discriminator then returns a probability between 0 and 1 of each letter being authentic. John can then use the results of the discriminator to create a more accurate generator. This loop continues, with both the generator and the discriminator learning from one another, until John achieves his desired result.
Uses for GAN machine learning
Some of the uses for GAN machine learning are:
Text to image
Professionals can use GAN learning to create meaningful images based on textual descriptions. The descriptions are often extremely specific to produce an intelligible image. This process can help create comics and animation, in which the GAN can produce a series of related images that convey a textual meaning.
Image to image
Another name for this GAN architecture is CycleGAN. It can map relationships between different images and change images based on the maps it creates. It can translate artistic images into realistic images, change a person's age in a photo or create a winter version of a summer image.
E-commerce and design
GAN learning can help manufacturers, designers and e-commerce platforms develop new products. It can use customer feedback and product response to design similar products that align with customer demand. This approach can be especially helpful when developing digitally designed products.
GAN machine learning can create very high-resolution images based on the analysis of photographs, and another name for this application is StyleGAN. One of the most common uses of StyleGAN is the creation of extremely realistic synthetic photos of the human face. The GAN architecture does this by learning human features from input data and aggregating it to create images of people who don't exist.
In this application, the GAN architecture can learn to correct images and remove unwanted elements. It can help restore photographs by repairing damage, filling in missing parts and removing scratches and other unwanted marks. It can also add details by adding missing pixels to simple images, which can make them more realistic.
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Advantages of GAN machine learning
GAN can provide several advantages over older types of machine learning. Here are some examples of these benefits:
Operating with little supervision: One of GAN's biggest benefits is that after the initial input, it continues to train itself by producing its training data.
Increasing data production: Because of its ability to autonomously imitate data distribution, GAN can produce highly specific collections of data very efficiently. It can often replace hours of human labor.
Improving data instances: GAN can improve the quality of data by comparing it with similar instances and making corrections.
Lowering costs: GAN can often replace hours of labor on the part of employees, which may help lower labor costs.
Challenges of GAN machine learning
Before starting with GAN machine learning, it’s important to understand some of the challenges it can pose, such as:
Complexity: Creating a GAN machine learning system requires strong technical knowledge and advanced data sets. If you are new to GANs, it’s useful to invest time in training and education so you can create an accurate model.
Result evaluations: When using GAN machine learning, it’s important to establish criteria for evaluating results, which may be challenging depending on the complexity or subjectivity of the task. In the previous simulated human handwriting example, John would need to know what makes one letter look more handwritten than another to measure the success of the GAN model.
Data availability: To accurately train a data set, GANs often need large data sets. If you don’t have a large enough data set available, you may receive inaccurate results from your GAN model, so being mindful of the data you input can help address inaccuracies.
Applications of GAN machine learning
GAN learning has applications in a growing number of fields, and some examples may help you understand its uses. Here are a few examples of what GAN learning can do:
Fill in images from outlines
Generate realistic images based on textual descriptions
Develop realistic images of product prototypes
Colorize black and white photographs, or convert color photos into grayscale
Restore damaged images
Age a person's photograph
Create realistic photographs
Transform photos into emojis
Edit or blend photographs
Predict motions for animation
Generate cartoon characters
Create original works of art
Change a summer scene into a winter scene
Transform a daylight scene into a night scene
Add details to maps
Generate frontal face photographs from angle photographs
Change human poses and facial expressions in photographs
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