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Journal articles on the topic 'CONDITIONAL GENERATIVE ADVERARIAL NETWORKS (CGAN)'

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Published: 11 September 2023

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1

Zhou, Guoqiang, Yi Fan, Jiachen Shi, Yuyuan Lu, and Jun Shen. "Conditional Generative Adversarial Networks for Domain Transfer: A Survey." Applied Sciences 12, no. 16 (2022): 8350. http://dx.doi.org/10.3390/app12168350.

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Generative Adversarial Network (GAN), deemed as a powerful deep-learning-based silver bullet for intelligent data generation, has been widely used in multi-disciplines. Furthermore, conditional GAN (CGAN) introduces artificial control information on the basis of GAN, which is more practical for many specific fields, though it is mostly used in domain transfer. Researchers have proposed numerous methods to tackle diverse tasks by employing CGAN. It is now a timely and also critical point to review these achievements. We first give a brief introduction to the principle of CGAN, then focus on how

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Lee, Minhyeok, and Junhee Seok. "Estimation with Uncertainty via Conditional Generative Adversarial Networks." Sensors 21, no. 18 (2021): 6194. http://dx.doi.org/10.3390/s21186194.

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Conventional predictive Artificial Neural Networks (ANNs) commonly employ deterministic weight matrices; therefore, their prediction is a point estimate. Such a deterministic nature in ANNs causes the limitations of using ANNs for medical diagnosis, law problems, and portfolio management in which not only discovering the prediction but also the uncertainty of the prediction is essentially required. In order to address such a problem, we propose a predictive probabilistic neural network model, which corresponds to a different manner of using the generator in the conditional Generative Adversari

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Zhang, Hao, and Wenlei Wang. "Imaging Domain Seismic Denoising Based on Conditional Generative Adversarial Networks (CGANs)." Energies 15, no. 18 (2022): 6569. http://dx.doi.org/10.3390/en15186569.

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A high-resolution seismic image is the key factor for helping geophysicists and geologists to recognize the geological structures below the subsurface. More and more complex geology has challenged traditional techniques and resulted in a need for more powerful denoising methodologies. The deep learning technique has shown its effectiveness in many different types of tasks. In this work, we used a conditional generative adversarial network (CGAN), which is a special type of deep neural network, to conduct the seismic image denoising process. We considered the denoising task as an image-to-image

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Zand, Jaleh, and Stephen Roberts. "Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN)." Signals 2, no. 3 (2021): 559–69. http://dx.doi.org/10.3390/signals2030034.

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Generative Adversarial Networks (GANs) have gained significant attention in recent years, with impressive applications highlighted in computer vision, in particular. Compared to such examples, however, there have been more limited applications of GANs to time series modeling, including forecasting. In this work, we present the Mixture Density Conditional Generative Adversarial Model (MD-CGAN), with a focus on time series forecasting. We show that our model is capable of estimating a probabilistic posterior distribution over forecasts and that, in comparison to a set of benchmark methods, the M

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Zhen, Hao, Yucheng Shi, Jidong J. Yang, and Javad Mohammadpour Vehni. "Co-supervised learning paradigm with conditional generative adversarial networks for sample-efficient classification." Applied Computing and Intelligence 3, no. 1 (2022): 13–26. http://dx.doi.org/10.3934/aci.2023002.

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<abstract> <p>Classification using supervised learning requires annotating a large amount of classes-balanced data for model training and testing. This has practically limited the scope of applications with supervised learning, in particular deep learning. To address the issues associated with limited and imbalanced data, this paper introduces a sample-efficient co-supervised learning paradigm (SEC-CGAN), in which a conditional generative adversarial network (CGAN) is trained alongside the classifier and supplements semantics-conditioned, confidence-aware synthesized examples to th

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Huang, Yubo, and Zhong Xiang. "A Metal Character Enhancement Method based on Conditional Generative Adversarial Networks." Journal of Physics: Conference Series 2284, no. 1 (2022): 012003. http://dx.doi.org/10.1088/1742-6596/2284/1/012003.

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Abstract In order to improve the accuracy and stability of metal stamping character (MSC) automatic recognition technology, a metal stamping character enhancement algorithm based on conditional Generative Adversarial Networks (cGAN) is proposed. We identify character regions manually through region labeling and Unsharpen Mask (USM) sharpening algorithm, and make the cGAN learn the most effective loss function in the adversarial training process to guide the generated model and distinguish character features and interference features, so as to achieve contrast enhancement between character and

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Kyslytsyna, Anastasiia, Kewen Xia, Artem Kislitsyn, Isselmou Abd El Kader, and Youxi Wu. "Road Surface Crack Detection Method Based on Conditional Generative Adversarial Networks." Sensors 21, no. 21 (2021): 7405. http://dx.doi.org/10.3390/s21217405.

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Constant monitoring of road surfaces helps to show the urgency of deterioration or problems in the road construction and to improve the safety level of the road surface. Conditional generative adversarial networks (cGAN) are a powerful tool to generate or transform the images used for crack detection. The advantage of this method is the highly accurate results in vector-based images, which are convenient for mathematical analysis of the detected cracks at a later time. However, images taken under established parameters are different from images in real-world contexts. Another potential problem

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Link, Patrick, Johannes Bodenstab, Lars Penter, and Steffen Ihlenfeldt. "Metamodeling of a deep drawing process using conditional Generative Adversarial Networks." IOP Conference Series: Materials Science and Engineering 1238, no. 1 (2022): 012064. http://dx.doi.org/10.1088/1757-899x/1238/1/012064.

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Abstract Optimization tasks as well as quality predictions for process control require fast responding process metamodels. A common strategy for sheet metal forming is building fast data driven metamodels based on results of Finite Element (FE) process simulations. However, FE simulations with complex material models and large parts with many elements consume extensive computational time. Hence, one major challenge in developing metamodels is to achieve a good prediction precision with limited data, while these predictions still need to be robust against varying input parameters. Therefore, th

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Falahatraftar, Farnoush, Samuel Pierre, and Steven Chamberland. "A Conditional Generative Adversarial Network Based Approach for Network Slicing in Heterogeneous Vehicular Networks." Telecom 2, no. 1 (2021): 141–54. http://dx.doi.org/10.3390/telecom2010009.

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Heterogeneous Vehicular Network (HetVNET) is a highly dynamic type of network that changes very quickly. Regarding this feature of HetVNETs and the emerging notion of network slicing in 5G technology, we propose a hybrid intelligent Software-Defined Network (SDN) and Network Functions Virtualization (NFV) based architecture. In this paper, we apply Conditional Generative Adversarial Network (CGAN) to augment the information of successful network scenarios that are related to network congestion and dynamicity. The results show that the proposed CGAN can be trained in order to generate valuable

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Aida, Saori, Junpei Okugawa, Serena Fujisaka, Tomonari Kasai, Hiroyuki Kameda, and Tomoyasu Sugiyama. "Deep Learning of Cancer Stem Cell Morphology Using Conditional Generative Adversarial Networks." Biomolecules 10, no. 6 (2020): 931. http://dx.doi.org/10.3390/biom10060931.

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Deep-learning workflows of microscopic image analysis are sufficient for handling the contextual variations because they employ biological samples and have numerous tasks. The use of well-defined annotated images is important for the workflow. Cancer stem cells (CSCs) are identified by specific cell markers. These CSCs were extensively characterized by the stem cell (SC)-like gene expression and proliferation mechanisms for the development of tumors. In contrast, the morphological characterization remains elusive. This study aims to investigate the segmentation of CSCs in phase contrast imagin

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Choi, Suyeon, and Yeonjoo Kim. "Rad-cGAN v1.0: Radar-based precipitation nowcasting model with conditional generative adversarial networks for multiple dam domains." Geoscientific Model Development 15, no. 15 (2022): 5967–85. http://dx.doi.org/10.5194/gmd-15-5967-2022.

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Abstract. Numerical weather prediction models and probabilistic extrapolation methods using radar images have been widely used for precipitation nowcasting. Recently, machine-learning-based precipitation nowcasting models have also been actively developed for relatively short-term precipitation predictions. This study was aimed at developing a radar-based precipitation nowcasting model using an advanced machine-learning technique, conditional generative adversarial network (cGAN), which shows high performance in image generation tasks. The cGAN-based precipitation nowcasting model, named Rad-c

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Yuan, Hao, Lei Cai, Zhengyang Wang, Xia Hu, Shaoting Zhang, and Shuiwang Ji. "Computational modeling of cellular structures using conditional deep generative networks." Bioinformatics 35, no. 12 (2018): 2141–49. http://dx.doi.org/10.1093/bioinformatics/bty923.

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Abstract Motivation Cellular function is closely related to the localizations of its sub-structures. It is, however, challenging to experimentally label all sub-cellular structures simultaneously in the same cell. This raises the need of building a computational model to learn the relationships among these sub-cellular structures and use reference structures to infer the localizations of other structures. Results We formulate such a task as a conditional image generation problem and propose to use conditional generative adversarial networks for tackling it. We employ an encoder–decoder network

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Thakur, Amey. "Generative Adversarial Networks." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 2307–25. http://dx.doi.org/10.22214/ijraset.2021.37723.

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Abstract: Deep learning's breakthrough in the field of artificial intelligence has resulted in the creation of a slew of deep learning models. One of these is the Generative Adversarial Network, which has only recently emerged. The goal of GAN is to use unsupervised learning to analyse the distribution of data and create more accurate results. The GAN allows the learning of deep representations in the absence of substantial labelled training information. Computer vision, language and video processing, and image synthesis are just a few of the applications that might benefit from these represen

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Cem Birbiri, Ufuk, Azam Hamidinekoo, Amélie Grall, Paul Malcolm, and Reyer Zwiggelaar. "Investigating the Performance of Generative Adversarial Networks for Prostate Tissue Detection and Segmentation." Journal of Imaging 6, no. 9 (2020): 83. http://dx.doi.org/10.3390/jimaging6090083.

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The manual delineation of region of interest (RoI) in 3D magnetic resonance imaging (MRI) of the prostate is time-consuming and subjective. Correct identification of prostate tissue is helpful to define a precise RoI to be used in CAD systems in clinical practice during diagnostic imaging, radiotherapy and monitoring the progress of disease. Conditional GAN (cGAN), cycleGAN and U-Net models and their performances were studied for the detection and segmentation of prostate tissue in 3D multi-parametric MRI scans. These models were trained and evaluated on MRI data from 40 patients with biopsy-p

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Green, Adrian J., Martin J. Mohlenkamp, Jhuma Das, et al. "Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology." PLOS Computational Biology 17, no. 7 (2021): e1009135. http://dx.doi.org/10.1371/journal.pcbi.1009135.

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There are currently 85,000 chemicals registered with the Environmental Protection Agency (EPA) under the Toxic Substances Control Act, but only a small fraction have measured toxicological data. To address this gap, high-throughput screening (HTS) and computational methods are vital. As part of one such HTS effort, embryonic zebrafish were used to examine a suite of morphological and mortality endpoints at six concentrations from over 1,000 unique chemicals found in the ToxCast library (phase 1 and 2). We hypothesized that by using a conditional generative adversarial network (cGAN) or deep ne

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Soni, Ayush, Alexander Loui, Scott Brown, and Carl Salvaggio. "High-quality multispectral image generation using Conditional GANs." Electronic Imaging 2020, no. 8 (2020): 86–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.8.imawm-086.

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In this paper, we demonstrate the use of a Conditional Generative Adversarial Networks (cGAN) framework for producing high-fidelity, multispectral aerial imagery using low-fidelity imagery of the same kind as input. The motivation behind is that it is easier, faster, and often less costly to produce low-fidelity images than high-fidelity images using the various available techniques, such as physics-driven synthetic image generation models. Once the cGAN network is trained and tuned in a supervised manner on a data set of paired low- and high-quality aerial images, it can then be used to enhan

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Huang, Yin-Fu, and Wei-De Liu. "Choreography cGAN: generating dances with music beats using conditional generative adversarial networks." Neural Computing and Applications 33, no. 16 (2021): 9817–33. http://dx.doi.org/10.1007/s00521-021-05752-x.

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Eom, Gayeong, and Haewon Byeon. "Searching for Optimal Oversampling to Process Imbalanced Data: Generative Adversarial Networks and Synthetic Minority Over-Sampling Technique." Mathematics 11, no. 16 (2023): 3605. http://dx.doi.org/10.3390/math11163605.

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Classification problems due to data imbalance occur in many fields and have long been studied in the machine learning field. Many real-world datasets suffer from the issue of class imbalance, which occurs when the sizes of classes are not uniform; thus, data belonging to the minority class are likely to be misclassified. It is particularly important to overcome this issue when dealing with medical data because class imbalance inevitably arises due to incidence rates within medical datasets. This study adjusted the imbalance ratio (IR) within the National Biobank of Korea dataset “Epidemiologic

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Li, Jie, Boyu Zhao, Kai Wu, Zhicheng Dong, Xuerui Zhang, and Zhihao Zheng. "A Representation Generation Approach of Transmission Gear Based on Conditional Generative Adversarial Network." Actuators 10, no. 5 (2021): 86. http://dx.doi.org/10.3390/act10050086.

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Gear reliability assessment of vehicle transmission has been a challenging issue of determining vehicle safety in the transmission industry due to a significant amount of classification errors with high-coupling gear parameters and insufficient high-density data. In terms of the preprocessing of gear reliability assessment, this paper presents a representation generation approach based on generative adversarial networks (GAN) to advance the performance of reliability evaluation as a classification problem. First, with no need for complex modeling and massive calculations, a conditional generat

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Li, Chen, Yuanbo Li, Zhiqiang Weng, Xuemei Lei, and Guangcan Yang. "Face Aging with Feature-Guide Conditional Generative Adversarial Network." Electronics 12, no. 9 (2023): 2095. http://dx.doi.org/10.3390/electronics12092095.

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Face aging is of great importance for the information forensics and security fields, as well as entertainment-related applications. Although significant progress has been made in this field, the authenticity, age specificity, and identity preservation of generated face images still need further discussion. To better address these issues, a Feature-Guide Conditional Generative Adversarial Network (FG-CGAN) is proposed in this paper, which contains extra feature guide module and age classifier module. To preserve the identity of the input facial image during the generating procedure, in the feat

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Zhang, Pengfei, and Xiaoming Ju. "Adversarial Sample Detection with Gaussian Mixture Conditional Generative Adversarial Networks." Mathematical Problems in Engineering 2021 (September 13, 2021): 1–18. http://dx.doi.org/10.1155/2021/8268249.

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It is important to detect adversarial samples in the physical world that are far away from the training data distribution. Some adversarial samples can make a machine learning model generate a highly overconfident distribution in the testing stage. Thus, we proposed a mechanism for detecting adversarial samples based on semisupervised generative adversarial networks (GANs) with an encoder-decoder structure; this mechanism can be applied to any pretrained neural network without changing the network’s structure. The semisupervised GANs also give us insight into the behavior of adversarial sample

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Ezeme, Okwudili M., Qusay H. Mahmoud, and Akramul Azim. "Design and Development of AD-CGAN: Conditional Generative Adversarial Networks for Anomaly Detection." IEEE Access 8 (2020): 177667–81. http://dx.doi.org/10.1109/access.2020.3025530.

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Kim, Hee-Joung, and Donghoon Lee. "Image denoising with conditional generative adversarial networks (CGAN) in low dose chest images." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 954 (February 2020): 161914. http://dx.doi.org/10.1016/j.nima.2019.02.041.

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Chrysos, Grigorios G., Jean Kossaifi, and Stefanos Zafeiriou. "RoCGAN: Robust Conditional GAN." International Journal of Computer Vision 128, no. 10-11 (2020): 2665–83. http://dx.doi.org/10.1007/s11263-020-01348-5.

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Abstract Conditional image generation lies at the heart of computer vision and conditional generative adversarial networks (cGAN) have recently become the method of choice for this task, owing to their superior performance. The focus so far has largely been on performance improvement, with little effort in making cGANs more robust to noise. However, the regression (of the generator) might lead to arbitrarily large errors in the output, which makes cGANs unreliable for real-world applications. In this work, we introduce a novel conditional GAN model, called RoCGAN, which leverages structure in

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Majid, Haneen, and Khawla Ali. "Expanding New Covid-19 Data with Conditional Generative Adversarial Networks." Iraqi Journal for Electrical and Electronic Engineering 18, no. 1 (2022): 103–10. http://dx.doi.org/10.37917/ijeee.18.1.12.

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COVID-19 is an infectious viral disease that mostly affects the lungs. That quickly spreads across the world. Early detection of the virus boosts the chances of patients recovering quickly worldwide. Many radiographic techniques are used to diagnose an infected person such as X-rays, deep learning technology based on a large amount of chest x-ray images is used to diagnose COVID-19 disease. Because of the scarcity of available COVID-19 X-rays image, the limited COVID-19 Datasets are insufficient for efficient deep learning detection models. Another problem with a limited dataset is that traini

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Ali, Zeeshan, Sheneela Naz, Hira Zaffar, Jaeun Choi, and Yongsung Kim. "An IoMT-Based Melanoma Lesion Segmentation Using Conditional Generative Adversarial Networks." Sensors 23, no. 7 (2023): 3548. http://dx.doi.org/10.3390/s23073548.

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Currently, Internet of medical things-based technologies provide a foundation for remote data collection and medical assistance for various diseases. Along with developments in computer vision, the application of Artificial Intelligence and Deep Learning in IOMT devices aids in the design of effective CAD systems for various diseases such as melanoma cancer even in the absence of experts. However, accurate segmentation of melanoma skin lesions from images by CAD systems is necessary to carry out an effective diagnosis. Nevertheless, the visual similarity between normal and melanoma lesions is

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Zaytar, Mohamed Akram, and Chaker El Amrani. "Satellite image inpainting with deep generative adversarial neural networks." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (2021): 121. http://dx.doi.org/10.11591/ijai.v10.i1.pp121-130.

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This work addresses the problem of recovering lost or damaged satellite image pixels (gaps) caused by sensor processing errors or by natural phenomena like cloud presence. Such errors decrease our ability to monitor regions of interest and significantly increase the average revisit time for all satellites. This paper presents a novel neural system based on conditional deep generative adversarial networks (cGAN) optimized to fill satellite imagery gaps using surrounding pixel values and static high-resolution visual priors. Experimental results show that the proposed system outperforms traditio

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Ku, Hyeeun, and Minhyeok Lee. "TextControlGAN: Text-to-Image Synthesis with Controllable Generative Adversarial Networks." Applied Sciences 13, no. 8 (2023): 5098. http://dx.doi.org/10.3390/app13085098.

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Generative adversarial networks (GANs) have demonstrated remarkable potential in the realm of text-to-image synthesis. Nevertheless, conventional GANs employing conditional latent space interpolation and manifold interpolation (GAN-CLS-INT) encounter challenges in generating images that accurately reflect the given text descriptions. To overcome these limitations, we introduce TextControlGAN, a controllable GAN-based model specifically designed for text-to-image synthesis tasks. In contrast to traditional GANs, TextControlGAN incorporates a neural network structure, known as a regressor, to ef

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Ma, Fei, Fei Gao, Jinping Sun, Huiyu Zhou, and and Amir Hussain. "Weakly Supervised Segmentation of SAR Imagery Using Superpixel and Hierarchically Adversarial CRF." Remote Sensing 11, no. 5 (2019): 512. http://dx.doi.org/10.3390/rs11050512.

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Synthetic aperture radar (SAR) image segmentation aims at generating homogeneous regions from a pixel-based image and is the basis of image interpretation. However, most of the existing segmentation methods usually neglect the appearance and spatial consistency during feature extraction and also require a large number of training data. In addition, pixel-based processing cannot meet the real time requirement. We hereby present a weakly supervised algorithm to perform the task of segmentation for high-resolution SAR images. For effective segmentation, the input image is first over-segmented int

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Rodríguez-Suárez, Brais, Pablo Quesada-Barriuso, and Francisco Argüello. "Design of CGAN Models for Multispectral Reconstruction in Remote Sensing." Remote Sensing 14, no. 4 (2022): 816. http://dx.doi.org/10.3390/rs14040816.

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Multispectral imaging methods typically require cameras with dedicated sensors that make them expensive. In some cases, these sensors are not available or existing images are RGB, so the advantages of multispectral processing cannot be exploited. To solve this drawback, several techniques have been proposed to reconstruct the spectral reflectance of a scene from a single RGB image captured by a camera. Deep learning methods can already solve this problem with good spectral accuracy. Recently, a new type of deep learning network, the Conditional Generative Adversarial Network (CGAN), has been p

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Ramazyan, T., O. Kiss, M. Grossi, E. Kajomovitz, and S. Vallecorsa. "Generating muonic force carriers events with classical and quantum neural networks." Journal of Physics: Conference Series 2438, no. 1 (2023): 012089. http://dx.doi.org/10.1088/1742-6596/2438/1/012089.

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Abstract Generative models (GM) are promising applications for near-term quantum computers due to the probabilistic nature of quantum mechanics. This work compares a classical conditional generative adversarial network (CGAN) with a quantum circuit Born machine while addressing their strengths and limitations to generate muonic force carriers (MFCs) events. The former uses a neural network as a discriminator to train the generator, while the latter takes advantage of the stochastic nature of measurements in quantum mechanics to generate samples. We consider a muon fixed-target collision betwee

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Jafrasteh, B., I. Manighetti, and J. Zerubia. "GENERATIVE ADVERSARIAL NETWORKS AS A NOVEL APPROACH FOR TECTONIC FAULT AND FRACTURE EXTRACTION IN HIGH-RESOLUTION SATELLITE AND AIRBORNE OPTICAL IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 1219–27. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1219-2020.

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Abstract. We develop a novel method based on Deep Convolutional Networks (DCN) to automate the identification and mapping of fracture and fault traces in optical images. The method employs two DCNs in a two players game: a first network, called Generator, learns to segment images to make them resembling the ground truth; a second network, called Discriminator, measures the differences between the ground truth image and each segmented image and sends its score feedback to the Generator; based on these scores, the Generator improves its segmentation progressively. As we condition both networks t

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Zhang, Zaijun, Hiroaki Ishihata, Ryuto Maruyama, Tomonari Kasai, Hiroyuki Kameda, and Tomoyasu Sugiyama. "Deep Learning of Phase-Contrast Images of Cancer Stem Cells Using a Selected Dataset of High Accuracy Value Using Conditional Generative Adversarial Networks." International Journal of Molecular Sciences 24, no. 6 (2023): 5323. http://dx.doi.org/10.3390/ijms24065323.

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Artificial intelligence (AI) technology for image recognition has the potential to identify cancer stem cells (CSCs) in cultures and tissues. CSCs play an important role in the development and relapse of tumors. Although the characteristics of CSCs have been extensively studied, their morphological features remain elusive. The attempt to obtain an AI model identifying CSCs in culture showed the importance of images from spatially and temporally grown cultures of CSCs for deep learning to improve accuracy, but was insufficient. This study aimed to identify a process that is significantly effici

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Yadav, Jyoti Deshwal, Vivek K. Dwivedi, and Saurabh Chaturvedi. "ResNet-Enabled cGAN Model for Channel Estimation in Massive MIMO System." Wireless Communications and Mobile Computing 2022 (August 29, 2022): 1–9. http://dx.doi.org/10.1155/2022/2697932.

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Massive multiple-input multiple-output (MIMO), or large-scale MIMO, is one of the key technologies for future wireless networks to exhibit a large accessible spectrum and throughput. The performance of a massive MIMO system is strongly reliant on the nature of various channels and interference during multipath transmission. Therefore, it is important to compute accurate channel estimation. This paper considers a massive MIMO system with one-bit analog-to-digital converters (ADCs) on each receiver antenna of the base station. Deep learning (DL)-based channel estimation framework has been develo

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Bittner, K., P. d’Angelo, M. Körner, and P. Reinartz. "AUTOMATIC LARGE-SCALE 3D BUILDING SHAPE REFINEMENT USING CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 103–8. http://dx.doi.org/10.5194/isprs-archives-xlii-2-103-2018.

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<p><strong>Abstract.</strong> Three-dimensional building reconstruction from remote sensing imagery is one of the most difficult and important 3D modeling problems for complex urban environments. The main data sources provided the digital representation of the Earths surface and related natural, cultural, and man-made objects of the urban areas in remote sensing are the <i>digital surface models (DSMs)</i>. The DSMs can be obtained either by <i>light detection and ranging (LIDAR)</i>, SAR interferometry or from stereo images. Our approach relies on aut

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List, Florian, Ishaan Bhat, and Geraint F. Lewis. "A black box for dark sector physics: predicting dark matter annihilation feedback with conditional GANs." Monthly Notices of the Royal Astronomical Society 490, no. 3 (2019): 3134–43. http://dx.doi.org/10.1093/mnras/stz2759.

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Abstract Traditionally, incorporating additional physics into existing cosmological simulations requires re-running the cosmological simulation code, which can be computationally expensive. We show that conditional Generative Adversarial Networks (cGANs) can be harnessed to predict how changing the underlying physics alters the simulation results. To illustrate this, we train a cGAN to learn the impact of dark matter annihilation feedback (DMAF) on the gas density distribution. The predicted gas density slices are visually difficult to distinguish from their real brethren and the peak counts d

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Yoshiura, Shintaro, Hayato Shimabukuro, Kenji Hasegawa та Keitaro Takahashi. "Predicting 21 cm-line map from Lyman-α emitter distribution with generative adversarial networks". Monthly Notices of the Royal Astronomical Society 506, № 1 (2021): 357–71. http://dx.doi.org/10.1093/mnras/stab1718.

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ABSTRACT The radio observation of 21 cm-line signal from the epoch of reionization (EoR) enables us to explore the evolution of galaxies and intergalactic medium in the early Universe. However, the detection and imaging of the 21 cm-line signal are tough due to the foreground and instrumental systematics. In order to overcome these obstacles, as a new approach, we propose to take a cross correlation between observed 21 cm-line data and 21 cm-line images generated from the distribution of the Lyman-α emitters (LAEs) through machine learning. In order to create 21 cm-line maps from LAE distribut

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Shao, Changcheng, Xiaolin Li, Fang Li, and Yifan Zhou. "Large Mask Image Completion with Conditional GAN." Symmetry 14, no. 10 (2022): 2148. http://dx.doi.org/10.3390/sym14102148.

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Recently, learning-based image completion methods have made encouraging progress on square or irregular masks. The generative adversarial networks (GANs) have been able to produce visually realistic and semantically correct results. However, much texture and structure information will be lost in the completion process. If the missing part is too large to provide useful information, the result will be ambiguity, residual shadow, and object confusion. In order to complete large mask images, we present a novel model using conditional GAN called coarse-to-fine condition GAN (CF CGAN). We use a coa

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Rastin, Zahra, Gholamreza Ghodrati Amiri, and Ehsan Darvishan. "Generative Adversarial Network for Damage Identification in Civil Structures." Shock and Vibration 2021 (September 3, 2021): 1–12. http://dx.doi.org/10.1155/2021/3987835.

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In recent years, many efforts have been made to develop efficient deep-learning-based structural health monitoring (SHM) methods. Most of the proposed methods employ supervised algorithms that require data from different damaged states of a structure in order to monitor its health conditions. As such data are not usually available for real civil structures, using supervised algorithms for the health monitoring of these structures might be impracticable. This paper presents a novel two-stage technique based on generative adversarial networks (GANs) for unsupervised SHM and damage identification

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Weng, Yongchun, Yong Ma, Fu Chen, et al. "Temporal Co-Attention Guided Conditional Generative Adversarial Network for Optical Image Synthesis." Remote Sensing 15, no. 7 (2023): 1863. http://dx.doi.org/10.3390/rs15071863.

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In the field of SAR-to-optical image synthesis, current methods based on conditional generative adversarial networks (CGANs) have satisfying performance under simple scenarios, but the performance drops severely under complicated scenarios. Considering that SAR images can form a robust time series due to SAR’s all-weather imaging ability, we take advantage of this and extract a temporal correlation from bi-temporal SAR images to guide the translation. To achieve this, we introduce a co-attention mechanism into the CGAN that learns the correlation between optically-available and optically-absen

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Sharafudeen, Misaj, Andrew J., and Vinod Chandra S. S. "Leveraging Vision Attention Transformers for Detection of Artificially Synthesized Dermoscopic Lesion Deepfakes Using Derm-CGAN." Diagnostics 13, no. 5 (2023): 825. http://dx.doi.org/10.3390/diagnostics13050825.

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Synthesized multimedia is an open concern that has received much too little attention in the scientific community. In recent years, generative models have been utilized in maneuvering deepfakes in medical imaging modalities. We investigate the synthesized generation and detection of dermoscopic skin lesion images by leveraging the conceptual aspects of Conditional Generative Adversarial Networks and state-of-the-art Vision Transformers (ViT). The Derm-CGAN is architectured for the realistic generation of six different dermoscopic skin lesions. Analysis of the similarity between real and synthe

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Li, Bing, Yong Xian, Juan Su, Da Q. Zhang, and Wei L. Guo. "I-GANs for Infrared Image Generation." Complexity 2021 (March 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/6635242.

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The making of infrared templates is of great significance for improving the accuracy and precision of infrared imaging guidance. However, collecting infrared images from fields is difficult, of high cost, and time-consuming. In order to address this problem, an infrared image generation method, infrared generative adversarial networks (I-GANs), based on conditional generative adversarial networks (CGAN) architecture is proposed. In I-GANs, visible images instead of random noise are used as the inputs, and the D-LinkNet network is also utilized to build the generative model, enabling improved l

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Yuan, X., J. Tian, and P. Reinartz. "GENERATING ARTIFICIAL NEAR INFRARED SPECTRAL BAND FROM RGB IMAGE USING CONDITIONAL GENERATIVE ADVERSARIAL NETWORK." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 279–85. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-279-2020.

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Abstract. Near infrared bands (NIR) provide rich information for many remote sensing applications. In addition to deriving useful indices to delineate water and vegetation, near infrared channels could also be used to facilitate image pre-processing. However, synthesizing bands from RGB spectrum is not an easy task. The inter-correlations between bands are not clearly identified in physical models. Generative adversarial networks (GAN) have been used in many tasks such as generating photorealistic images, monocular depth estimation and Digital Surface Model (DSM) refinement etc. Conditional GA

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Rojas-Campos, Adrian, Michael Langguth, Martin Wittenbrink, and Gordon Pipa. "Deep learning models for generation of precipitation maps based on numerical weather prediction." Geoscientific Model Development 16, no. 5 (2023): 1467–80. http://dx.doi.org/10.5194/gmd-16-1467-2023.

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Abstract. Numerical weather prediction (NWP) models are atmospheric simulations that imitate the dynamics of the atmosphere and provide high-quality forecasts. One of the most significant limitations of NWP is the elevated amount of computational resources required for its functioning, which limits the spatial and temporal resolution of the outputs. Traditional meteorological techniques to increase the resolution are uniquely based on information from a limited group of interest variables. In this study, we offer an alternative approach to the task where we generate precipitation maps based on

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Lee, JooHwa, and KeeHyun Park. "AE-CGAN Model based High Performance Network Intrusion Detection System." Applied Sciences 9, no. 20 (2019): 4221. http://dx.doi.org/10.3390/app9204221.

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In this paper, a high-performance network intrusion detection system based on deep learning is proposed for situations in which there are significant imbalances between normal and abnormal traffic. Based on the unsupervised learning models autoencoder (AE) and the generative adversarial networks (GAN) model during deep learning, the study aim is to solve the imbalance of data and intrusion detection of high performance. The AE-CGAN (autoencoder-conditional GAN) model is proposed to improve the performance of intrusion detection. This model oversamples rare classes based on the GAN model in ord

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Hong, Zhiwei, Xiaocheng Fan, Tao Jiang, and Jianxing Feng. "End-to-End Unpaired Image Denoising with Conditional Adversarial Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4140–49. http://dx.doi.org/10.1609/aaai.v34i04.5834.

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Image denoising is a classic low level vision problem that attempts to recover a noise-free image from a noisy observation. Recent advances in deep neural networks have outperformed traditional prior based methods for image denoising. However, the existing methods either require paired noisy and clean images for training or impose certain assumptions on the noise distribution and data types. In this paper, we present an end-to-end unpaired image denoising framework (UIDNet) that denoises images with only unpaired clean and noisy training images. The critical component of our model is a noise l

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Al-Shargabi, Amal A., Jowharah F. Alshobaili, Abdulatif Alabdulatif, and Naseem Alrobah. "COVID-CGAN: Efficient Deep Learning Approach for COVID-19 Detection Based on CXR Images Using Conditional GANs." Applied Sciences 11, no. 16 (2021): 7174. http://dx.doi.org/10.3390/app11167174.

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COVID-19, a novel coronavirus infectious disease, has spread around the world, resulting in a large number of deaths. Due to a lack of physicians, emergency facilities, and equipment, medical systems have been unable to treat all patients in many countries. Deep learning is a promising approach for providing solutions to COVID-19 based on patients’ medical images. As COVID-19 is a new disease, its related dataset is still being collected and published. Small COVID-19 datasets may not be sufficient to build powerful deep learning detection models. Such models are often over-fitted, and their pr

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Luo, Qingli, Hong Li, Zhiyuan Chen, and Jian Li. "ADD-UNet: An Adjacent Dual-Decoder UNet for SAR-to-Optical Translation." Remote Sensing 15, no. 12 (2023): 3125. http://dx.doi.org/10.3390/rs15123125.

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Synthetic aperture radar (SAR) imagery has the advantages of all-day and all-weather observation. However, due to the imaging mechanism of microwaves, it is difficult for nonexperts to interpret SAR images. Transferring SAR imagery into optical imagery can better improve the interpretation of SAR data and support the further fusion research of multi-source remote sensing. Methods based on generative adversarial networks (GAN) have been proven to be effective in SAR-to-optical translation tasks. To further improve the translation results of SAR data, we propose a method of an adjacent dual-deco

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Rizkinia, Mia, Nathaniel Faustine, and Masahiro Okuda. "Conditional Generative Adversarial Networks with Total Variation and Color Correction for Generating Indonesian Face Photo from Sketch." Applied Sciences 12, no. 19 (2022): 10006. http://dx.doi.org/10.3390/app121910006.

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Historically, hand-drawn face sketches have been commonly used by Indonesia’s police force, especially to quickly describe a person’s facial features in searching for fugitives based on eyewitness testimony. Several studies have been performed, aiming to increase the effectiveness of the method, such as comparing the facial sketch with the all-points bulletin (DPO in Indonesian terminology) or generating a facial composite. However, making facial composites using an application takes quite a long time. Moreover, when these composites are directly compared to the DPO, the accuracy is insufficie

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Ramdani, Ahmad, Andika Perbawa, Ingrid Puspita, and Volker Vahrenkamp. "Acoustic impedance to outcrop: Presenting near-surface seismic data as a virtual outcrop in carbonate analog studies." Leading Edge 41, no. 9 (2022): 599–610. http://dx.doi.org/10.1190/tle41090599.1.

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Outcrop analogs play a central role in understanding subseismic interwell depositional facies heterogeneity of carbonate reservoirs. Outcrop geologists rarely utilize near-surface seismic data due to the limited vertical resolution and difficulty visualizing seismic signals as “band-limited rocks.” This study proposes a methodology using a combination of forward modeling and conditional generative adversarial network (cGAN) to translate seismic-derived acoustic impedance (AI) into a pseudo-high-resolution virtual outcrop. We tested the methodology on the Hanifa reservoir analog outcropping in

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