Realistic River Image Synthesis Using Deep Generative Adversarial Networks

Multi-source information fused generative adversarial network model and data assimilation based history matching for reservoir with complex geologies

Event prediction is essential in social network (SN) analysis to study the SN's evolutionary patterns (communities). Machine learning (ML) models are often used to predict events in SN communities. The ML algorithms manifest the results with biasness for the same dataset. Hence, the performance of an ML model requires validation for the unseen data to avoid biasness in learning. Generally, researchers use the generative adversarial network (GAN) model for generating realistic sample data to enhance the prediction of events. It is challenging for the discriminator to learn features using a single layer with similar weights in a conventional GAN technique. Therefore, this article proposes an improved version of the GAN model named generative adversarial network classifier (GAN‐C). The proposed GAN‐C model contains an additional layer, called classifier, that generates different feature maps. Wherein weights are adjusted dynamically based on the conditions to predict the events. To be precise, conditioning the weights in a classifier layer using entropy values of the features is a simple and effective way to minimize the classifier loss function (categorical cross‐entropy). GAN‐C model results in 16% loss up to 10 batches, and after that, the loss becomes negligible and can generate non‐overlapping events. The gaps between such non‐overlapping events are analyzed using the Jensen–Shannon divergence technique. The experimental results show that the existing single‐GAN and multi‐GAN methods predict events with 79.34% and 82.17% accuracy, respectively. While the proposed GAN‐C comparatively predicts events with improved accuracy of 88.56% on the same dataset. The data generated by the GAN‐C model are also approximately 55.85% and 80.59% more realistic than multi‐GAN and single‐GAN, respectively, based on root mean square error and inception score comparisons. GAN‐C is also approximately 68.01% faster than other GAN models. Thus, this article's theoretical and experimental analysis justifies that GAN‐C works suitably for predicting events in a massive dataset.

Nucleic acids are the basic units of deoxyribonucleic acid (DNA) sequencing. Every organism demonstrates different DNA sequences with specific nucleotides. It reveals the genetic information carried by a particular DNA segment. Nucleic acid sequencing expresses the evolutionary changes among organisms and revolutionizes disease diagnosis in animals. This paper proposes a generative adversarial networks (GAN) model to create synthetic nucleic acid sequences of the cat genome tuned to exhibit specific desired properties. We obtained the raw sequence data from Illumina next generation sequencing. Various data preprocessing steps were performed using Cutadapt and DADA2 tools. The processed data were fed to the GAN model that was designed following the architecture of Wasserstein GAN with gradient penalty (WGAN-GP). We introduced a predictor and an evaluator in our proposed GAN model to tune the synthetic sequences to acquire certain realistic properties. The predictor was built for extracting samples with a promoter sequence, and the evaluator was built for filtering samples that scored high for motif-matching. The filtered samples were then passed to the discriminator. We evaluated our model based on multiple metrics and demonstrated outputs for latent interpolation, latent complementation, and motif-matching. Evaluation results showed our proposed GAN model achieved 93.7% correlation with the original data and produced significant outcomes as compared to existing models for sequence generation.

Smart fire detection analysis in complex building floorplans powered by GAN

To train generative adversarial network (GAN) models to generate predictive optical coherence tomography (OCT) images of central serous chorioretinopathy (CSC) at 3months after observation using multi-modal OCT images. Four hundred forty CSC eyes of 440 patients who underwent Cirrus OCT imaging were included. Baseline OCT B-scan images through the foveal center, en face choroid, and en face ellipsoid zone were collected from each patient. The datasets were divided into training and validation (n = 390) and test (n = 50) sets. The input images for each model comprised either baseline B-scan alone or a combination of en face choroid and ellipsoid zones. Predictive post-treatment OCT B-scan images were generated using GAN models and compared with real 3-month images. Of 50 generated OCT images, there were 48, 47, and 48 acceptable images for UNIT, CycleGAN, and RegGAN, respectively. In comparison with real 3-month images, the generated images showed sensitivity, specificity, and positive predictive values (PPV) for residual fluid in the ranges of 0.762-1.000, 0.483-0.724, and 0.583-0.704; for pigment epithelial detachment (PED) of 0.917-1.000, 0.974-1.000, and 0.917-1.000; and for subretinal hyperreflective material (SHRM) of 0.667-0.778, 0.925-0.950 and 0.700-0.750, respectively. RegGAN exhibited the highest values except for sensitivity. GAN models could generate prognostic OCT images with good performance for prediction of residual fluid, PED, and SHRM presence in CSC. Implementation of the models may help predict disease activity in CSC, facilitating the establishment of a proper treatment plan.

The original generative adversarial network (GAN) model may struggle to adequately capture global information in images, particularly during complex decomposition tasks, leading to limitations in image clarity, detail retention and overall consistency. To address this challenge, we propose the large separable kernel attention generative adversarial network (LSKA‐GAN) model, building upon the blind image decomposition network (BIDeN). The LSKA module enhances BIDeN's global information capturing capability, thereby improving the quality and clarity of generated images. Experimental results demonstrate that LSKA‐GAN achieves obvious improvements in hybrid image decomposition. Compared to BIDeN, LSKA‐GAN exhibits an increase of 1.39 dB in peak signal‐to‐noise ratio (PSNR) and 0.04 in structural similarity index (SSIM). These improvements enable LSKA‐GAN to generate clearer images with more complete details, marking a notable advancement in image decomposition technology.

With the rapid development of the Internet, application interface design has undergone rapid changes. Numerous new design styles and resources have appeared; thus, a large number of interface icon design needs have been generated. Icons are quite different from ordinary photographed images, because they are all drawn by designers and have certain schematic and artistic features. Moreover, artistic icons can convey their drawn characteristics and meanings faster and better than captured images. The ideation process in icon design is time-consuming, and its design style and method of drawing are influenced by the device and the environment in which it is used. To simplify the process of icon design and enrich the creativity of icon conception, this study proposes to use the generative adversarial network technology in deep learning to train computers to generate artistic icons. This paper completes the construction of the icon generation model with generative adversarial network (GAN) model combined with the actual icon design process. For the problem of automatic icon generation, this paper does the following research work: (1) based on the conditional classification generative adversarial network, a multifeature icon generation model (MFIGM) is proposed. In the discriminator, a multifeature identification module is added to optimize the structure of the conditional feature to ensure that the icon generated by the model meets the given conditional feature. (2) Experiments on the icon dataset show that the MFIGM-based icon generation model proposed in this paper has better performance in designing various feature expressions of icons.

Apparel designers and manufacturers are now using virtual garment simulation technology to evaluate 3D prototypes in virtual environments, which reduces the waste of raw materials in the sampling process. With the immediate visualization of the 3D prototypes, designers and manufacturers can communicate with each other to adjust the virtual garments seamlessly, and thereby the production process has become simplified using simulation technologies. Nevertheless, there are several limitations to the current practice. Apparel companies do not have a universal sizing standard, which leads to problems because customers need to identify their sizes in different stores, and a high return rate is expected in this case. Additionally, the psychological preferences of the wearers are not taken into account when evaluating fit. Production teams in apparel companies are only concerned with the physical fit of their targeted customer groups; they neglect the actual will of a specific customer. For example, some may like to wear oversized garments, and a just-fit size is not what they want. It is valuable to find a method to adapt the psychographic orientations of the customers to the design and production process of a garment. Therefore, we had proposed our method for developing a virtual garment fitting prediction model to predict the garment pattern parameters with anthropometric data and psychographic orientations of subjects, and previous work had proven that the prediction model has high accuracy and stability. Nevertheless, a limitation was found in that the subject data was difficult to obtain. It would be advantageous if there were more data to test in the prediction model. Thus, this study proposes the build of generative adversarial network (GAN) models to generate new body dimension data and garment parameter data. The new datasets produced by the GAN models would be favourable for an improvement in the virtual garment fitting prediction model with more training and testing data to be processed. Moreover, the synthetic datasets can be employed by designers to do research in their garment evaluation process since they have more data on similar body dimensions and preferred garment sizes to assess. A more comprehensive appraisal of the garment fit can be attained by this approach, which accelerates the design process in the apparel production stage.

Recently, the hospital systems face a high influx of patients generated by several events, such as seasonal flows or health crises related to epidemics (e.g., COVID’19). Despite the extent of the care demands, hospital establishments, particularly emergency departments (EDs), must admit patients for medical treatments. However, the high patient influx often increases patients’ length of stay (LOS) and leads to overcrowding problems within the EDs. To mitigate this issue, hospital managers need to predict the patient’s LOS, which is an essential indicator for assessing ED overcrowding and the use of the medical resources (allocation, planning, utilization rates). Thus, accurately predicting LOS is necessary to improve ED management. This paper proposes a deep learning-driven approach for predicting the patient LOS in ED using a generative adversarial network (GAN) model. The GAN-driven approach flexibly learns relevant information from linear and nonlinear processes without prior assumptions on data distribution and significantly enhances the prediction accuracy. Furthermore, we classified the predicted patients’ LOS according to time spent at the pediatric emergency department (PED) to further help decision-making and prevent overcrowding. The experiments were conducted on actual data obtained from the PED in Lille regional hospital center, France. The GAN model results were compared with other deep learning models, including deep belief networks, convolutional neural network, stacked auto-encoder, and four machine learning models, namely support vector regression, random forests, adaboost, and decision tree. Results testify that deep learning models are suitable for predicting patient LOS and highlight GAN’s superior performance than the other models.

With the advancement of artificial intelligence (AI) techniques, fake digital content has mushroomed in recent years. The production of very realistic fake content, even by a novice computer literate, has become a cakewalk with the latest deep learning and generative adversarial network (GAN) models. The objectionable content created by the use of deepfake technology is getting exploited by bad elements to tarnish the image of celebrities. With the advancement of digital technologies, it is almost impossible to detect fake content with naked eyes and is posing a big challenge for the public. In this manuscript, we use a method to detect deepfakes by using eye reflection and comparing both eyes in terms of different metrics such as structural similarity index measure (SSIM), intersection over union (IoU), and mean-squared error (MSE). We used Flickr-Faces-HQ (FFHQ) dataset for real human eye images, and the thispersondosentexist.com Web site for GAN-synthesized images. Next, we used automatic detection of the corneal region and compared pixels of both eyes. Despite their simplicity, the methods can achieve AUC values of up to 0.9.KeywordsDeepfakeGANsDeep learningGAN image detection

The lack of the sufficient and diverse training data is one of the main challenges limiting performances of the machine learning enabled applications in optical networks. Here, we propose a deep learning based sequential data augmentation technique for the aggregate traffic data augmentation for diverse optical network scenarios. A generative adversarial network (GAN) model is trained with the experimental traffic data to automatically extract the substantial characteristics of the experimental traffic data through the zero-sum game theory and then augment the traffic data adaptively. The statistical evaluation parameters of the augmented traffic are mean, variance and Hurst exponent. To add comparisons, two other classical generative models including the statistical parameter configuration (SPC) model and the variational autoencoder (VAE) model are also adopted to generate the traffic data that are similar to the actual traffic data. The comprehensive comparisons among the proposed GAN, the SPC and VAE show that the performances of the GAN exceed those of the SPC and the VAE obviously. The mean and the variance of the augmented traffic data from the GAN are almost equal to those of the experimental traffic data, where the average deviations are both within 2%. The Hurst exponent of the augmented traffic data from the GAN is respectively near 90% and 96% of those of the experimental traffic data in the access network and the core network. To estimate the similarity intuitively, the well-known k-mean algorithm is used to cluster the augmented traffic data according to the centroids determined by the corresponding experimental traffic data and the clustering accuracies are all higher than 95% for 6 kinds of typical traffic types in the optical networks. These results demonstrate that the proposed GAN is able to effectively generate the traffic data that is very close to the experimental traffic data and is difficult to be distinguished for diverse traffic types. Moreover, a relatively small dataset with a few hundred pieces of experimental traffic data is required and the amount of the augmented traffic data from the GAN is unlimited in theory, which can be augmented as much as we need. The proposed traffic data augmentation technique also has the potential to be utilized in other sequential data augmentation applications for the optical networks.

The application of the Generative Adversarial Networks (GANs) model has increased rapidly in the past few years. This paper will demonstrate the training results of different GANs models in different epochs. Since GANs is a structure, they can be applied to different neural network layers such as Convolutional Neural Network (CNN) and multilayer perceptron (MLP). Different neural network layers are constructed and trained separately to compare their performance and efficiency in this experiment. To achieve this goal, all models perform the same task, generating handwritten numbers. In the experiment, three GANs models are tested. Two of them are based on MLP. They use the same structure and the same number of units. The only difference is the activation function. One uses the sigmoid function, and the other uses the LeakyRelu function. The last model is based on CNN. Specifically, the GANs model using Convolutional layers is called DCGAN. All models are constructed using TensorFlow, each of them was trained for 10, 20, 40, 80, 120, and 160 epochs. The generated images and loss value are recorded in all cases of the previously mentioned epochs of training. The experimental results demonstrated that Compared with the GANs model based on MLP, DCGAN has higher training efficiency and better image quality. Different activation functions can also affect the efficiency of the GANs model.

Image dehazing has always been a challenging topic in image processing. The development of deep learning methods, especially the generative adversarial networks (GAN), provides a new way for image dehazing. In recent years, many deep learning methods based on GAN have been applied to image dehazing. However, GAN has two problems in image dehazing. Firstly, For haze image, haze not only reduces the quality of the image but also blurs the details of the image. For GAN network, it is difficult for the generator to restore the details of the whole image while removing the haze. Secondly, GAN model is defined as a minimax problem, which weakens the loss function. It is difficult to distinguish whether GAN is making progress in the training process. Therefore, we propose a guided generative adversarial dehazing network (GGADN). Different from other generation adversarial networks, GGADN adds a guided module on the generator. The guided module verifies the network of each layer of the generator. At the same time, the details of the map generated by each layer are strengthened. Network training is based on the pre-trained VGG feature model and L1-regularized gradient prior which is developed by new loss function parameters. From the dehazing results of synthetic images and real images, the proposed method is better than the state-of-the-art dehazing methods.

In this study, a bias field correction workflow was proposed to improve the flexibility and generalizability of the generative adversarial network (GAN) model for abdominal cancer patients treated with a 0.35T magnetic resonance imaging linear accelerator (MR-LINAC) system. Model training was performed using brain MR images acquired on a 3T diagnostic scanner, while model testing was performed using abdominal MR images obtained using a 0.35T MR-LINAC system. The performance of the proposed workflow was first compared with the GAN model using root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). To assess the impact of the workflow on image segmentation, it was also compared with the N4ITK algorithm. Segmentation was performed using the k-means clustering algorithm with three clusters corresponding to air, fat, and soft tissue. Segmentation accuracy was then evaluated using the Dice similarity coefficient (DSC). The RMSE values were 30.59, 12.06, 10.37 for the bias field-corrupted images (IIN), GAN-corrected images (IGAN), and images corrected with the proposed workflow (IOUT), respectively. Corresponding PSNR values were 42.34, 46.04, 47.04 dB, and SSIM values were 0.84, 0.96, 0.98. For segmentation accuracy, the mean DSC for air masks was 0.95, 0.97, and 0.97; for fat masks, 0.61, 0.71, and 0.74; and for soft tissue masks, 0.60, 0.68, and 0.69, corresponding to IIN, N4ITK-corrected images (IN4ITK), and IOUT, respectively CONCLUSION: By effectively mitigating bias field artifacts, the proposed workflow has the potential to strengthen the clinical utility of MRI-guided adaptive radiotherapy for abdominal cancers, ensuring safer and more accurate radiation delivery.

Flooding is one of the most frequent natural hazards and causes more economic loss than all the other natural hazards. Fast and accurate flood prediction has significance in preserving lives, minimizing economic damage, and reducing public health risks. However, current methods cannot achieve speed and accuracy simultaneously. Numerical methods can provide high-fidelity results, but they are time-consuming, particularly when pursuing high accuracy. Conversely, neural networks can provide results in a matter of seconds, but they have shown low accuracy in flood map generation by all existing methods. This work combines the strengths of numerical methods and neural networks and builds a framework that can quickly and accurately model the high-fidelity flood inundation map with detailed water depth information. In this paper, we employ the U-Net and generative adversarial network (GAN) models to recover the lost physics and information from ultra-fast, low-resolution numerical simulations, ultimately presenting high-resolution, high-fidelity flood maps as the end results. In this study, both the U-Net and GAN models have proven their ability to reduce the computation time for generating high-fidelity results, reducing it from 7–8 h down to 1 min. Furthermore, the accuracy of both models is notably high.