A generalized optimization-based generative adversarial network

Farhadinia, Bahram; Ahangari, Mohammad Reza; Heydari, Aghileh; Datta, Amitava

doi:10.1016/j.eswa.2024.123413

Cited by 1 publication

(3 citation statements)

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“…Machine learning has emerged as a powerful tool in this domain, allowing meteorologists to develop more sophisticated models that can process and interpret complex atmospheric data. By leveraging this technology, researchers have been able to improve the accuracy of weather forecasts, better understand climate phenomena, and develop early warning systems for severe weather events [22][23][24].…”

Section: Third Part: Comparison Of Gan-generated Imagesmentioning

confidence: 99%

“…The GAN structure of this contribution has two convolutional neural networks: The structures of both the generator and discriminator are illustrated in Figures 4 and 5, as shown in [22]. Reference [31] contains color images with a size of 512 × 512 pixels.…”

Section: Third Part: Comparison Of Gan-generated Imagesmentioning

confidence: 99%

“…In the other part of the experiment, similar to the previous case, we examine the similarity measure values for binarized images using the Sogno integral, Choquet integral, and Bradley method. In the last experiment, we utilize a generative adversarial network (GAN) [22][23][24] to investigate the cloud cover subject. We generate images and assess the similarity between the generated images and the original ones.…”

Section: Introductionmentioning

confidence: 99%

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Utilizing Generative Adversarial Networks Using a Category of Fuzzy-Based Structural Similarity Indices for Constructing Datasets in Meteorology

Farhadinia,

Ahangari,

Heydari

2024

Mathematics

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Machine learning and image processing are closely related fields that have undergone major development and application in recent years. Machine learning algorithms are being used to develop sophisticated techniques for analyzing and interpreting images, such as object detection, image classification, and image segmentation. One important aspect of image processing is the ability to compare and measure the similarity between different images by providing a way to quantify the similarity between images using various features such as contrast, luminance, and structure. Generally, the flexibility of similarity measures enables fine-tuning the comparison process to achieve the desired outcomes. This is while the existing similarity measures are not flexible enough to address diverse and comprehensive practical aspects. To this end, we utilize triangular norms (t-norms) to construct an inclusive class of similarity measures in this article. As is well-known, each t-norm possesses distinctive attributes that allow for novel interpretations of image similarities. The proposed class of t-norm-based structural similarity measures offers numerous options for decisionmakers to consider various issues and interpret results more broadly in line with their objectives. For more details, in the Experiments section, the proposed method is applied to grayscale and binarized images and a specific experiment related to meteorology. Eventually, the presented diverse case studies confirm the efficiency and key features of the t-norm-based structural similarity.

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Section: Third Part: Comparison Of Gan-generated Imagesmentioning

confidence: 99%

Section: Third Part: Comparison Of Gan-generated Imagesmentioning

confidence: 99%