Our research focuses on optimizing the performance of Generative Adversarial Networks to increase image resolution through various methods and approaches. The limitations posed by current digital storage mediums poses a unique problem which may be resolved through the use of Generative Adversarial Networks (GANs)
Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably...
The generative adversarial network (GAN) has demonstrated superb performance in generating synthetic...
Super-resolution is one of the frequently investigated methods of image processing. The quality of t...
Low-resolution image enhancement has long been in the public’s consciousness. Television shows, movi...
Low-resolution image enhancement has long been in the public’s consciousness. Television shows, movi...
The research focuses on applying Generative Adversarial Networks (GANs) [1] to enhance the clarity o...
In recent years, the interest in image super-resolution is increased sharply developing the field of...
In recent years, Generative Adversarial Network (GAN) and its variants have gained great popularity ...
The research to find new ways to improve Generative Adversarial Networks (GANs) and ways to evaluate...
Since their introduction in 2014 Generative Adversarial Networks (GANs) have been employed successfu...
Computer vision is one of the hottest research fields in deep learning. The emergence of generative ...
The article is an in-depth analysis of two leading approaches in the field of generative modeling: g...
GANs (generative opposing networks) are a technique for learning deep representations in the absence...
Generative Adversarial Network is the topic of interest in today’s research in the field of image pr...
In recent years, although Optical Character Recognition (OCR) has made considerable progress, low-re...
Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably...
The generative adversarial network (GAN) has demonstrated superb performance in generating synthetic...
Super-resolution is one of the frequently investigated methods of image processing. The quality of t...
Low-resolution image enhancement has long been in the public’s consciousness. Television shows, movi...
Low-resolution image enhancement has long been in the public’s consciousness. Television shows, movi...
The research focuses on applying Generative Adversarial Networks (GANs) [1] to enhance the clarity o...
In recent years, the interest in image super-resolution is increased sharply developing the field of...
In recent years, Generative Adversarial Network (GAN) and its variants have gained great popularity ...
The research to find new ways to improve Generative Adversarial Networks (GANs) and ways to evaluate...
Since their introduction in 2014 Generative Adversarial Networks (GANs) have been employed successfu...
Computer vision is one of the hottest research fields in deep learning. The emergence of generative ...
The article is an in-depth analysis of two leading approaches in the field of generative modeling: g...
GANs (generative opposing networks) are a technique for learning deep representations in the absence...
Generative Adversarial Network is the topic of interest in today’s research in the field of image pr...
In recent years, although Optical Character Recognition (OCR) has made considerable progress, low-re...
Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably...
The generative adversarial network (GAN) has demonstrated superb performance in generating synthetic...
Super-resolution is one of the frequently investigated methods of image processing. The quality of t...