Generating Third-Person View from First-Person View in Driving

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.

Smart fire detection analysis in complex building floorplans powered by GAN

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.

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.

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.

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.

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.

Accurate recognition of characters imprinted on ship bodies is essential for ensuring operational efficiency, safety, and security in the maritime industry. However, the limited availability of datasets of specialized digits and characters poses a challenge. To overcome this challenge, we propose a generative adversarial network (GAN) model for augmenting the limited dataset of special digits and characters in ship markings. We evaluated the performance of various GAN models, and the Wasserstein GAN with Gradient Penalty (WGAN-GP) and Wasserstein GAN with divergence (WGANDIV) models demonstrated exceptional performance in generating high-quality synthetic images that closely resemble the original imprinted characters required for augmenting the limited datasets. And the evaluation metric, Fréchet inception distance, further validated the outstanding performance of the WGAN-GP and WGANDIV models, establishing them as optimal choices for dataset augmentation to enhance the accuracy and reliability of recognition systems.

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

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.

Towards automated 3D evaluation of water leakage on a tunnel face via improved GAN and self-attention DL model

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.

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.

Accurate modulation identification of the received signals is undoubtedly a central component in multiple-input multiple-output (MIMO) communication systems, facilitating the demodulation task. This study presents a flexible and semi-supervised deep learning-driven strategy for automatic modulation identification. To this end, the multiclass classification problem is treated as multiple binary discrimination problems to address modulation identification challenges. Here, we merge the features extraction ability of the Generative Adversarial Network (GAN) model and the semi-supervised anomaly detection scheme, the one-class Support Vector Machine (1SVM). Essentially, a single GAN-based 1SVM detector is trained using training data of each class, with the samples of that class as inlier and all other samples as anomalies (i.e., one-vs.-rest). The 1SVM is trained using the features learned by the GAN model. A dataset consisting of three digital modulations (i.e., BFSK, CPFSK, and PAM4) and three analog modulations (i.e., AM-DSB, AM-SSB, and WB-FM), widely used in wireless communications systems, is employed to demonstrate the performance of the considered deep learning-based methods. Compared to Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN)-based 1SVM, the conventional GAN, DBN, and RBM with softmax layer as discriminator layer, the proposed GAN-based 1SVM detector offers superior discrimination performance of modulation types by achieving an averaged accuracy of 0.951 and F1-Score of 0.954. Results also showed that the GAN-1SVM detector dominates the state-of-the-art modulation classification techniques.

Evaluating solar radiation distribution at the urban scale is crucial for optimizing the placement and size of solar installations and managing urban heat. This study introduces a method for predicting urban solar radiation using 2D mapping data, applying a Generative Adversarial Network (GAN) model to the city of Boston. Traditional solar radiation simulation methods, such as 3D modeling and satellite imagery, require complex and resource-intensive data inputs. In contrast, this research allows open-source 2D urban geographic information—such as building footprints, heights, and terrain—to predict solar radiation at various spatial scales (150 m, 300 m, and 500 m). The GAN model, using detailed 3D urban modeling and simulation results, trained paired datasets of geographic information and solar radiation heatmaps. It achieved high accuracy and resolution, with the 300 m scale model demonstrating the best performance (R2 = 0.864). The model’s capability to generate high-resolution (2 m) solar radiation maps from simplified inputs demonstrates the potential of GANs for urban climate data prediction, offering a rapid and efficient alternative to traditional methods. This approach holds significant potential for urban planning, particularly in optimizing photovoltaic (PV) system layouts and managing the UHI effect.