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29INTERNATIONAL SCIENTIFIC AND TECHNICAL CONFERENCE "DIGITAL TECHNOLOGIES: PROBLEMS AND SOLUTIONS OF PRACTICAL IMPLEMENTATION IN THE SPHERES" APRIL 27-28, 2023
THEME: REVIEW TYPES OF GAN METHOD FOR IMAGE SUPER-RESOLUTION
Shohruh Begmatov \ Mukhriddin Arabboev 2, Khabibullo Nosirov 3, Mokhirjon
Rikhsivoev 4
1,2 Television and Radio Broadcasting Systems Department,3 Dean of Radio and mobile communications faculty, ^Electronics and Radiotechnics Department, 1,2,3,4 Tashkent University of Information Technologies named after Muhammad al-Khwarizmi,
Uzbekistan https://doi.org/10.5281/zenodo.7854589
Abstract: In recent years, the interest in image super-resolution is increased sharply developing the field of research in Image processing. Several types of methods are used to improve the quality of images. Each image super-resolution methods have their usages cases, benefits and drawbacks.
In this paper, we reviewed the methods of the GAN (Generative Adversarial Network) overview, working principles, architecture and types, benefits and drawbacks and their applications. One approach to generative modelling using deep learning techniques, such as convolutional neural networks, is known as generative adversarial networks/ GANs [1].
Keywords: Image super-resolution, Generative Adversarial Network (GAN), Natural Language Processing (NLP).
1. Introduction
Generative modelling is a machine learning activity that includes automatically identifying and learning the regularities or patterns in incoming data such that the model may be used to produce new instances that might have been reasonably derived from the original dataset. By framing the challenge as a supervised learning problem with two sub-models—the generator model, which we train to create new instances, and the discriminator model, which tries to categorize examples as either real (from the domain) or fake—GANs are a creative method to train a generative model [2].
Generative Adversarial Networks (GANs) consist of two neural networks, a generator and a discriminator, that are trained together in a game-like setting. The generator is a neural network that generates new data samples by mapping random noise to a data distribution. The generator takes as input a vector of random noise and produces an output that is intended to look like a sample from the training data.
During training, the generator is optimized to generate samples that are indistinguishable from real data by the discriminator. The generator is trained to minimize the loss function, which measures the difference between the generated samples and the real samples. The goal is to train the generator to produce samples that are so realistic that the discriminator cannot tell the difference between real and generated samples.
The generator is typically made up of multiple layers of neurons, such as fully connected layers or convolutional layers, and can be designed to generate various types of data, such as images, text, and music. The architecture and hyperparameters of the generator can have a significant impact on the quality of the generated samples[3].
The discriminator is a neural network that has been trained to discriminate between samples of actual data and those that were produced. The discriminator is given both real samples from the
INTERNATIONAL SCIENTIFIC AND TECHNICAL CONFERENCE "DIGITAL TECHNOLOGIES: PROBLEMS AND SOLUTIONS OF PRACTICAL IMPLEMENTATION IN THE SPHERES" APRIL 27-28, 2023
training data and generated samples from the generator, and its task is to predict whether each sample is real or generated.
During training, the discriminator is optimized to correctly classify real samples as real and generated samples as generated. The discriminator is trained to maximize the loss function, which measures the difference between the predicted output of the discriminator and the true label of the sample (real or generated).
The generator is trained to minimize the loss function, while the discriminator is trained to maximize it. The goal is to train the generator to produce samples that are so realistic that the discriminator cannot tell the difference between real and generated samples.
The discriminator is typically made up of multiple layers of neurons, such as fully connected layers or convolutional layers, and can be designed to classify various types of data, such as images, text, and music. The architecture and hyperparameters of the discriminator can have a significant impact on the quality of the generated samples [4].
Training Feedback
Generated sample
Real sample
Figure-1. Structure of Generative Adversarial Networks (GANs) algorithm [5].
During the training process, the generator receives random input data (i.e., noise) and generates a data sample. The discriminator then evaluates the generated sample and provides feedback to the generator on how to improve its output to better resemble real data. The generator uses this feedback to adjust its output, and the process is repeated until the generator can generate data samples that the discriminator cannot distinguish from real data.
The GAN training process is iterative and adversarial, with the generator and discriminator networks competing with each other to improve their performance. As the training progresses, the generator becomes better at generating realistic data, while the discriminator becomes better at distinguishing between real and fake data [6].
2. Advantages and disadvantages of GAN
It is difficult to provide a comprehensive list of advantages and disadvantages for all types of GANs since there are many variations and applications of GANs, each with its own unique strengths and weaknesses. However, here are some general advantages and disadvantages of GANs that may apply to various types:
Benefits Drawbacks
GANs can generate high-quality data samples that are similar to the training data, which can be useful in many applications. Can be difficult to train, as the generator and discriminator networks need to be carefully balanced to avoid mode collapse
INTERNATIONAL SCIENTIFIC AND TECHNICAL CONFERENCE "DIGITAL TECHNOLOGIES: PROBLEMS AND SOLUTIONS OF PRACTICAL IMPLEMENTATION IN THE SPHERES" APRIL 27-28, 2023
They can be used to generate new data samples that can be used to augment existing datasets and improve model performance.
Can learn from unlabeled data, which can be useful in cases where labeled data is scarce.
GANs are highly flexible and can be adapted to a wide range of applications. They can be used for various tasks, such as image and video synthesis, text generation, and anomaly detection_
(i.e. when the generator learns to generate only a few types of samples). They can be computationally expensive and require large amounts of data and computing resources. Can suffer from instability and can be sensitive to hyperparameter settings. The quality of the generated data is highly dependent on the quality of the training data, and GANs may struggle to generate data that is significantly different from the training data._
Table-1. Plus and minus points of Generative Adversarial Networks (GANs)
These advantages and disadvantages may apply to various types of GANs, but it's important to keep in mind that each type of GAN may have its own unique strengths and weaknesses depending on the specific application [1].
3. Types of Generative Adversarial Networks (GANs)
There are several types of GANs, each designed for specific purposes. Here are a few examples:
Vanilla GAN: This is the simplest form of GAN, where the generator and discriminator are fully connected neural networks. It is used for generating simple data such as images and audio.
Conditional GAN: This type of GAN allows you to condition the generator on a specific input, such as a class label or a sentence. It is used for tasks such as image-to-image translation and text-to-image generation.
Wasserstein GAN: This type of GAN uses the Wasserstein distance to measure the distance between the real and generated data distributions. It is more stable than the vanilla GAN and can generate higher-quality images.
CycleGAN: This type of GAN is used for image-to-image translation, where the goal is to translate images from one domain to another. It utilizes a cycle-consistency loss to make sure the produced images match the input photos accurately.
StyleGAN: This type of GAN is used for generating high-quality images with realistic textures and details. It uses a progressive training scheme and a style-based generator architecture to generate highly realistic images.
Progressive GAN: This type of GAN is used for generating high-resolution images in a progressive manner. It starts by generating low-resolution images and gradually increases the resolution until the desired resolution is reached.
These are just a few examples of the many types of GANs that have been developed for various applications [7].
4. Applications of Generative Adversarial Networks (GANs)
In this part, we reviewed GANs applications which are widely used in recent years. Here is a more comprehensive list of the applications of GANs:
No Name Definition
1. Image and Video Generation GANs can generate realistic images and videos that can be used in various fields such as entertainment, gaming, and virtual reality.
INTERNATIONAL SCIENTIFIC AND TECHNICAL CONFERENCE "DIGITAL TECHNOLOGIES: PROBLEMS AND SOLUTIONS OF PRACTICAL IMPLEMENTATION IN THE SPHERES" APRIL 27-28, 2023
2. Image and Video Editing GANs can be used for image and video editing tasks such as style transfer, image inpainting, and image super-resolution.
3. Medical Imaging GANs can generate synthetic medical images for training and testing medical image analysis algorithms, and can also be used for generating synthetic data to protect patient privacy.
4. Natural Language Processing (NLP) For applications like text summarization, question answering, and conversation production, GANs may provide realistic text and language data.
5. Data Augmentation GANs can generate synthetic data that can be used for data augmentation, improving the performance of machine learning models.
6. Art and Design GANs can be used for generating artwork, music, and other creative content.
7. Robotics GANs can generate synthetic images and videos that can be used for training robots to perform tasks in various environments.
8. Cybersecurity Machine learning models for identifying and preventing cyberattacks may be trained using synthetic data produced by GANs.
9. Virtual Try-On GANs can generate images of people wearing clothes or accessories, allowing customers to try on items virtually before purchasing.
10. Interior Design GANs can generate images of interior spaces with different furniture and decor options, allowing designers and homeowners to visualize different arrangements.
11. Advertising GANs can generate images and videos for advertising campaigns, allowing marketers to create more personalized and engaging content.
12. Fashion and Textiles GANs can generate images and patterns for clothing and textile designs, allowing designers to create unique and customizable products.
13. Autonomous Vehicles Autonomous vehicles may be taught in a variety of locations thanks to GANs, which can produce synthetic photos and videos of diverse driving scenarios.
14. Astronomy GANs can generate synthetic images of galaxies and other astronomical objects, allowing astronomers to study them in more detail.
15. Music Generation GANs can generate new musical compositions, allowing musicians and composers to explore new creative possibilities.
Table-2. Applications of Generative Adversarial Networks
Overall, GANs have the potential to revolutionize various fields by generating realistic and high-quality data, improving the performance of machine learning models, and enhancing creative content generation.
5. CONCLUSION
In this paper, we reviewed an overview of GANs concepts, architecture, and applications. Generative Adversarial Networks (GANs) are a powerful class of neural networks that have been used to generate realistic data samples in various fields such as computer vision, natural language
INTERNATIONAL SCIENTIFIC AND TECHNICAL CONFERENCE "DIGITAL TECHNOLOGIES: PROBLEMS AND SOLUTIONS OF PRACTICAL IMPLEMENTATION IN THE SPHERES" APRIL 27-28, 2023
processing, and music generation. GANs consist of two neural networks - a generator and a discriminator - that are trained in an adversarial manner to generate realistic data samples.
The generator generates new data samples by mapping random noise to a data distribution, while the discriminator is trained to distinguish between real and generated samples. During training, the generator is optimized to generate samples that are indistinguishable from real data, while the discriminator is optimized to correctly classify real and generated samples.
Overall, GANs are a promising area of research that has the potential to revolutionize various fields and generate significant advancements in artificial intelligence and machine learning.
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