This is one of the modern technologies that offer great potential in many use cases. It helps to create new pictures, amplify the voice, and provide various applications in medicine and other industries. This advanced technology can help shape products and services. It can also be used to enhance image quality to preserve memories. What are we talking about? About generative adversarial networks, of course. Let’s find out more.
Generative adversarial networks meaning
Generative Adversarial Network (GAN) is an ML framework consisting of two neural networks. Both networks will compete against each other to generate more accurate predictions. The two competing blocs in GAN are:
- Generator – it is a CNN that manufactures artificial outputs similar to real data.
- Discriminator – it is a deconvolutional neural network that can identify artificially created outputs.
Generative models create their training data. The generator generates artificial data, and the discriminator learns how to distinguish the real ones from the fake ones. If the discriminator detects false data, the generator “suffers a penalty”. This feedback loop will continue until the generator generates better, more convincing output data and the discriminator is better at catching false data.
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Types of GANs
Several types of Generative Adversarial Networks (GANs) have been used for different purposes. Here are some of the most common types.
VANILLA GAN
This is the original GAN proposed by Ian Goodfellow in 2014. It consists of a generator network and a discriminator network that compete with each other during training.
DEEP CONVOLUTIONAL GAN (DCGAN)
It is popular and considered the most successful implementation of GAN. DCGAN consists of ConvNets, not multilayer perceptrons. These ConvNets are deployed without the use of techniques such as max-pooling or full-layer bonding.
CONDITIONAL GAN
It is a deep learning technique that includes specific conditional parameters. It helps distinguish real from fake data. It also includes an additional parameter, i.e., “Y,” in the generator phase to produce the corresponding data. Besides, labels are added to this input and fed to the discriminator to enable it to verify whether the data is genuine or false.
LAPLACE’S PYRAMID GAN (LAPGAN)
LAPGAN uses several discriminator and generator networks and multiple levels of Laplace pyramids. LAPGAN is widely used because it provides the highest image quality.
STYLEGANS
StyleGANs are used for image synthesis. It enables users to control specific aspects of the generated images, such as style, color, and texture.
BIGGANS
BigGANs are a type of GAN that use a large number of parameters to generate high-quality images.
The architecture of GANs
GANs consist of two main modules: a generator and a discriminator. The generator is designed to produce data samples from random noise vectors that are supposed to resemble the original data. The discriminator, on the other hand, receives both the original and generated data and has the task of distinguishing one from the other.
The learning process is an iterative game between the generator and the discriminator. The generator tries to produce the most realistic data possible. In turn, the discriminator tries to distinguish this data from the original.
During each iteration, the discriminator receives both the original and generated data. The generator is then trained on the errors made by the discriminator to improve the quality of the samples it generates.
As training continues, the generator improves its ability to create more realistic data. It makes it harder for the discriminator to differentiate between generated and original data.
Popular applications of GANs
VIDEO GAMES
The video game industry uses GAN to scale up the low-resolution 2-dimensional data used in older video games. It will help you reproduce such data in 4K or even at a higher resolution through image training. You can then sample the data or images to match the actual resolution of the video game.
ART AND FASHION
You can use GAN to generate art, such as images of people who never existed, photographs in paint, creating images of unreal models, and much more. It is also used in drawings that generate virtual shadows and sketches.
SOUND SYNTHESIS
You can create audio files from a set of audio clips using GAN. This is also called generative sound. Generative sound uses neural networks to study the statistical properties of a sound source. It then directly recreates those properties in a given context. Here, the modeling shows how speech changes every millisecond.
Conclusion
GAN is one of the most extraordinary examples of modern technology. It provides a unique and better way to generate data. It also supports functions such as visual diagnosis, image synthesis, research, data augmentation, arts and science, and much more. This article explained what GANs are, how they work, and what types there are. It also outlines some of the uses.