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Applications of GANs

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In this article, we have explored the different applications of GANs such as Image Inpainting, Steganography and much more. A Generative Adversarial Network, or GAN, is a generative modeling neural network architecture.

Generative modeling is the process of using a model to produce new examples that are reasonable to originate from an existing distribution of samples, for producing new images that are comparable but distinct from a dataset of existing photographs. The GAN was introduced by Ian Goodfellow in 2014.

Table of contents:

  1. Applications of GANs
  2. Image inpainting with GANs
  3. Using GANs for Steganography (SSGAN)
  4. Generating Synthetic Data
  5. Image Super Resolution Using GANs
  6. Image translation
  7. 3D Object Generation using GANs

Applications of GANs

GANs have a fairly specialized set of applications. It's time to go into some of the most fascinating GAN applications now in use in the business.

The different applications of GANs are:

  • Image inpainting with GANs
  • Using GANs for Steganography (SSGAN)
  • Generating Synthetic Data
  • Image Super Resolution Using GANs
  • Image translation
  • 3D Object Generation using GANs

Image inpainting with GANs

The field of computer science is creating plenty of advancement. Image inpainting is also creating enormous progress since the introduction of the Generative Adversarial network in 2014. The common drawback with image inpainting is the fuzziness and unreasonable structure. For this, the solution is Generative Adversarial Nets based on the paper from Ian Goodfellow. According to the paper the two models are trained simultaneously: a generative model G that captures the data distribution and a discriminative model D that calculates the chance that a sample came from the training data rather than G. The goal of G's training is to increase the chances of D making a mistake.

Image inpainting is a technique for recreating missing elements of an image, and it can also be used to restore missing parts of films.
This approach is a key aspect of computer vision that may be applied to a variety of image and graphics applications, such as image editing i.e. filling in or erasing areas of an image using a smart touch brush.

This is especially effective for restoring beautiful antique scratched photos.
Previously, the missing sections were filled using the patch matching method.
Where the patches were found iteratively and the best ones were stitched into the image's damaged areas.

It frequently reconstructs the image using smaller, more straightforward damages with a comparable texture.However, when the damage is more severe or intricate, the issue develops. It is then unable to generate a credible output. Artificial intelligence is advancing at a rapid pace these days. The deep convolutional neural network is a type of neural network that is used to create complicated neural networks. It performs complex analysis on enormous amounts of data that are transmitted through numerous layers of the neural network. In the realm of computer vision, it's mostly used for image classification and object detection. Since its introduction in 2014, deep convolutional networks and Generative Adversarial Networks (GANs) have produced incredibly impressive results in image inpainting. Generative Adversarial Networks is a model for creating whole new data sets.
The goal is to develop a model that produces the reconstructed images that look more detailed and realistic.Also the completed image in the sense of facial reconstruction should be more finely detailed and approaching ground truth as much as possible.

The main idea behind filling the missing pixels in a image involves two types of information the first one is contextual information and another one is perceptual information With contextual information we can contemplate the what could be possible pixels for missing ones using surrounding pixels. and with perceptual information the pixels can be filled with the portions like from other pictures. Both of these go hand in hand as both are important and give valid completions for a context. Developing an algorithm consisting of both of these can be obtained by using statistics and machine learning.

Digital images are in the form of pixels. Pixels in an image are generally represented by a byte if it is grayscale and by 3 bytes if it is a color image (RGB). Image and statistics can be related as images can be interpreted as samples from high dimensional probability distribution. By using the maximization problem we can find the unknown value of pixels. This method is only applicable for simple distributions over images not suitable for complex distribution over images as the process becomes quite difficult

So we will generate new random samples using a generative model. According to the paper "Generative Adversarial Nets," by Ian Goodfellow, the GAN is made up of two neural networks: a generator network (G) and a discriminator network (D), both of which have possible convolutional layers. The discriminator receives an image and attempts to determine whether it is real or fake, whereas the generator receives Gaussian noise and generates an image in order to deceive the discriminator into believing the fake image is real. There are two steps in each training iteration. To maximize its ability to correctly differentiate between fake and real images, the discriminator is given a batch of real data from the unlabeled training dataset and another batch of fake data generated by the generator, and the discriminator updates its parameters using gradient descent, just like in the image classification case. Second, the generator takes Gaussian noise and creates a series of fictitious images. The discriminator receives the batch of bogus photographs and expresses its belief that the images are genuine. The generator then uses gradient descent to update its parameters, increasing the discriminator's confidence that they are real images when they aren't. Only one portion of the GAN is used in each phase. , either the discriminator or the generator, updates its parameters. As the discriminator improves at distinguishing between real and fake photos, the generator is forced to create more realistic images in order to fool the discriminator.

Our goal is for the generator to produce phony images that are indistinguishable from actual photographs by the end of the training. The discriminator returns numbers between 0 and 1. It wants to output numbers near to 1 for real photos and close to 0 for fake images, thus (D(real images)-1)2 + D(G(gaussian noise))2 would be a good loss function.

The generator wants the discriminator to produce numbers near to 1 for the bogus images it generates, therefore (D(G(gaussian noise))-1)2 is a good loss function.

Using GANs for Steganography (SSGAN)

Researchers attempted to build stegonographic schemes using GANs,One generator and two discriminators were utilized in this study.The generator's goal is to create images that are both aesthetically consistent and resistant to steganalysis tools in order to hide information. Secure cover pictures are what they're called.

Discriminators carry out two tasks:One incorporates a GAN-based steganalysis framework, which the authors believe is more complex than previous studies' frameworks.The second "competes" with the generator to stimulate diversity in the generated images โ€” that is, it tries to judge the proposed image's visual quality.
As a result, the generator will no longer output noisy images.Instead, it receives feedback indicating which photos are more visually appropriate.The second discriminator tries to figure out whether the photos are suitable for steganography.

Generating Synthetic Data

Creating images is difficult enough, but generating synthetic data might be even more difficult. A lot of existing statistics and deep neural networks models fail to property model data. As privacy restrictions become more stringent, the opportunity to use synthetic data is rapidly expanding. Synthetic data can be utilized in any situation where access to data containing personally identifiable information is not required. Many people, however, expect the synthetic data to show the same relationships as the actual data. Existing statistical models and anonymization technologies frequently have negative consequences for data quality in downstream tasks such as classification. GANs, a deep learning-based synthesization technique, give solutions for scenarios when maintaining these relationships is critical.

Image Super Resolution Using GANs

The goal of "a simple technique employing GANs" is to build a Higher Resolution image from a Lower Resolution image while retaining as much detail as feasible.
In other words, it's the process of resampling a previously undersampled image.
At the moment, hardware advancements are mainly reliant on the ability to capture higher-resolution photographs.

Although many digital cameras are capable of capturing HR photos, the cost of developing and purchasing such a high-end camera is prohibitive.

Furthermore, many computer vision applications, such as medical imaging, forensics, and others, continue to have a high demand for higher resolution imagery, which is likely to be surpassed by today's HR digital cameras' capabilities.

Image translation

For translating data from photos, generative adversarial networks can be employed. Image-to-image translations, semantic image-to-photo translations, and text-to-image translations can all benefit from GANs. Translations from one image to another: GANs can be used in image-to-image translations to perform jobs such as:

  • Using Google Maps to convert satellite photos.
  • Changing the elements of a scene from day to night and vice versa.
  • Adding color to black-and-white images.
  • Converting sketching into color photos.

3D Object Generation using GANs

In recent years, generative adversarial networks (GANs) have made substantial progress in 3D model generation and reconstruction. By sampling from a homogeneous noise distribution, GANs may create 3D models. Researchers have had success with a technology called 3D-GAN approach, which uses neural networks to create realistic 3-D models from 2-D pictures by converting a "latent representation" of a 2D image into a 3D model.

As we read in this article at OpenGenus, Generative adversarial networks offer a wide range of applications, and with continued research and development, they are positioned to help a wide range of businesses.

Applications of GANs
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