Inverse metacluster design using generative modeling for minimal scattering response

2021 ◽  
Vol 263 (1) ◽  
pp. 4990-4999
Author(s):  
Peter Lai ◽  
Feruza Amirkulova
Keyword(s):  
The Novel ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Generative Modeling ◽  
Recognition Ability ◽  
Total Scattering Cross Section ◽  
Scattering Response ◽  
Incident Waves ◽  
Optimal Configurations ◽  
Better Than

Metamaterials are subwavelength-sized artificial structures with the ability to manipulate incident waves in such a way that affects how the energy propagates throughout the medium. In acoustics, particularly placed scattering elements can reduce the total scattering cross section (TSCS) response. We propose a method to inversely design acoustic metamaterial configurations using deep learning and generative modeling. Using our proprietary multiple scattering solver with MATLAB optimization toolbox, we generate a dataset of optimal configurations with minimized TSCS within a discrete range of wavenumbers. We use this dataset to train a Conditional Wasserstein Generative Adversarial Network (cWGAN) to generate similar metacluster designs corresponding to specified input TSCS. To improve the coordinate recognition ability of the cWGAN, we include the novel CoordConv layer in the generator and critic. After training, the cWGAN can produce a variety of optimal configurations given an expected TSCS. Evaluating TSCS of generated configurations shows that the model is capable of proposing scatterer configurations that are comparable or better than the dataset within the optimized range.

scholarly journals 3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Images

2021 ◽  
pp. 24-34
Author(s):  
Sungmin Hong ◽  
Razvan Marinescu ◽  
Adrian V. Dalca ◽  
Anna K. Bonkhoff ◽  
Martin Bretzner ◽  
...  
Keyword(s):  
Medical Images ◽  
Three Dimensional ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Generative Modeling

scholarly journals Semi-supervised generative adversarial networks for anomaly detection

2022 ◽  
Vol 132 ◽  
pp. 01016
Author(s):  
Juan Montenegro ◽  
Yeojin Chung
Keyword(s):  
Anomaly Detection ◽  
Video Surveillance ◽  
Daily Life ◽  
Generative Adversarial Networks ◽  
Gamma Correction ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Different Types ◽  
Better Than

Advancements in security have provided ways of recording anomalies of daily life through video surveillance. For the present investigation, a semi-supervised generative adversarial network model to detect and classify different types of crimes on videos. Additionally, we intend to tackle one of the most recurring difficulties of anomaly detection: illumination. For this, we propose a light augmentation algorithm based on gamma correction to help the semi-supervised generative adversarial networks on its classification task. The proposed process performs slightly better than other proposed models.

scholarly journals Generative Enhancement of 3D Image Classifiers

2020 ◽  
Vol 10 (21) ◽  
pp. 7433
Author(s):  
Michal Varga ◽  
Ján Jadlovský ◽  
Slávka Jadlovská
Keyword(s):  
Neural Networks ◽  
Knowledge Sharing ◽  
Classification Accuracy ◽  
Network Architecture ◽  
Deep Neural Networks ◽  
3D Image ◽  
Accuracy Improvement ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Generative Modeling

In this paper, we propose a methodology for generative enhancement of existing 3D image classifiers. This methodology is based on combining the advantages of both non-generative classifiers and generative modeling. Its purpose is to streamline the synthesis of novel deep neural networks by embedding existing compatible classifiers into a generative network architecture. A demonstration of this process and evaluation of its effectiveness is performed using a 3D convolutional classifier and its generative equivalent—a 3D conditional generative adversarial network classifier. The results of the experiments show that the generative classifier delivers higher performance, gaining a relative classification accuracy improvement of 7.43%. An increase of accuracy is also observed when comparing it to a plain convolutional classifier that was trained on a dataset augmented with samples created by the trained generator. This suggests a desirable knowledge sharing mechanism exists within the hybrid discriminator-classifier network.

scholarly journals Generative Enhancement of 3D Image Classifiers

2020 ◽  
Author(s):  
Michal Varga ◽  
Ján Jadlovský ◽  
Slávka Jadlovská
Keyword(s):  
Knowledge Sharing ◽  
Classification Accuracy ◽  
Network Architecture ◽  
Classification Performance ◽  
Generative Model ◽  
3D Image ◽  
Accuracy Improvement ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Generative Modeling

Abstract In this paper, we propose a methodology for generative enhancement of existing 3D image classifiers. This methodology is based on combining the strengths of both non-generative classifiers and generative modeling. Its purpose is to streamline the creation of new classifiers by embedding existing compatible classifiers in a generative network architecture. The demonstration of this process and evaluation of its effects is performed using a 3D convolutional classifier and its generative equivalent - a conditional generative adversarial network classifier. The results show that the generative model achieves greater classification performance, gaining a relative classification accuracy improvement of 7.43%. Improvement of accuracy is also present when compared to a plain convolutional classifier trained on a dataset augmented with examples produced by a trained generator. This suggests there is a desirable knowledge sharing within the hybrid discriminator-classifier network.

Underwater acoustic target classification based on u-shaped network

2021 ◽  
Vol 263 (5) ◽  
pp. 1338-1345
Author(s):  
Xue Lingzhi ◽  
Zeng Xiangyang
Keyword(s):  
Network Architecture ◽  
Feature Maps ◽  
Generative Adversarial Network ◽  
Underwater Acoustic ◽  
Adversarial Network ◽  
Label Information ◽  
Acoustic Target ◽  
Feature Information ◽  
Underwater Acoustic Signal ◽  
Better Than

Target recognition is a key task and a difficult technique in underwater acoustic signal processing. One of the most challenging problem is that the label information of the underwater acoustic samples is scarce or missing. To solve the problem, this paper presents a local skip connection u-shaped architecture network(U-Net)based on the convolutional neural network(CNN).To this end, the network architecture is designed cleverly to generate a contracting path and an expansive path to achieve the extraction of different scale features. More importantly, a local skip connection mechanism is proposed to optimize classification rates by reusing former feature maps in contracting path. The experimental results of the measured dataset demonstrate the recognition accuracy of the model is better than that of deep belief network(DBN) and generative adversarial network(GAN) networks.Further research on three kinds of network by visualization method shows that the proposed network can learn more effective feature information with limited samples.

scholarly journals A Fault Diagnosis Method for Rolling Bearings Based on Parameter Transfer Learning under Imbalance Data Sets

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 944
Author(s):  
Cheng Peng ◽  
Lingling Li ◽  
Qing Chen ◽  
Zhaohui Tang ◽  
Weihua Gui ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Hot Spot ◽  
Data Sets ◽  
Target Domain ◽  
Rolling Bearings ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Recognition Ability ◽  
Diagnosis Method

Fault diagnosis under the condition of data sets or samples with only a few fault labels has become a hot spot in the field of machinery fault diagnosis. To solve this problem, a fault diagnosis method based on deep transfer learning is proposed. Firstly, the discriminator of the generative adversarial network (GAN) is improved by enhancing its sparsity, and then adopts the adversarial mechanism to continuously optimize the recognition ability of the discriminator; finally, the parameter transfer learning (PTL) method is applied to transfer the trained discriminator to target domain to solve the fault diagnosis problem with only a small number of label samples. Experimental results show that this method has good fault diagnosis performance.

scholarly journals Generative adversarial networks for reconstructing natural images from brain activity

2017 ◽  
Author(s):  
K. Seeliger ◽  
U. Güçlü ◽  
L. Ambrogioni ◽  
Y. Güçlütürk ◽  
M. A. J. van Gerven
Keyword(s):  
Brain Activity ◽  
Pairwise Comparison ◽  
Natural Images ◽  
Generative Adversarial Networks ◽  
Natural Image ◽  
Semantic Features ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Generative Modeling ◽  
Reconstruction Performance

AbstractWe explore a method for reconstructing visual stimuli from brain activity. Using large databases of natural images we trained a deep convolutional generative adversarial network capable of generating gray scale photos, similar to stimUli presented during two functional magnetic resonance imaging experiments. Using a linear model we learned to predict the generative model’s latent space from measured brain activity. The objective was to create an image similar to the presented stimulus image through the previously trained generator. Using this approach we were able to reconstruct structural and some semantic features of a proportion of the natural images sets. A behavioral test showed that subjects were capable of identifying a reconstruction of the original stimuhis in 67.2% and 66.4% of the cases in a pairwise comparison for the two natural image datasets respectively. our approach does not require end-to-end training of a large generative model on limited neuroimaging data. Rapid advances in generative modeling promise further improvements in reconstruction performance.

scholarly journals Low-Dose CT Image Denoising with Improving WGAN and Hybrid Loss Function

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhihua Li ◽  
Weili Shi ◽  
Qiwei Xing ◽  
Yu Miao ◽  
Wei He ◽  
...  
Keyword(s):  
Loss Function ◽  
Low Dose ◽  
Structural Similarity ◽  
Image Method ◽  
Ct Image ◽  
Generative Adversarial Network ◽  
Low Dose Ct ◽  
Adversarial Network ◽  
Quantitative Measurements ◽  
Better Than

The X-ray radiation from computed tomography (CT) brought us the potential risk. Simply decreasing the dose makes the CT images noisy and diagnostic performance compromised. Here, we develop a novel denoising low-dose CT image method. Our framework is based on an improved generative adversarial network coupling with the hybrid loss function, including the adversarial loss, perceptual loss, sharpness loss, and structural similarity loss. Among the loss function terms, perceptual loss and structural similarity loss are made use of to preserve textural details, and sharpness loss can make reconstruction images clear. The adversarial loss can sharp the boundary regions. The results of experiments show the proposed method can effectively remove noise and artifacts better than the state-of-the-art methods in the aspects of the visual effect, the quantitative measurements, and the texture details.

scholarly journals Nucleus Image Segmentation Method Based on GAN Network and FCN Model

2021 ◽  
Author(s):  
Kai Zhang ◽  
Yang Shi ◽  
Chengquan Hu ◽  
Hang Yu
Keyword(s):  
Image Segmentation ◽  
Cell Nucleus ◽  
Data Science ◽  
Generative Adversarial Network ◽  
Convolutional Network ◽  
Adversarial Network ◽  
Segmentation Methods ◽  
A Cell ◽  
Fully Connected ◽  
Better Than

Abstract Aiming at the problems of rough edges and low accuracy in processing cell nucleus image segmentation in existing image segmentation methods. A cell nucleus image segmentation technology based on generative adversarial network (GAN) network and fully convolutional network (FCN) model is proposed. First, the FCN model is used to perform preliminary segmentation of the cell nucleus image, in which the fully connected layer convolution and skip connection are used to improve the accuracy of image segmentation. Then, improve the GAN network, introduce splitting branches into the discriminator structure, and combine the GAN network and the splitting network into one. At the same time, pixel loss is introduced in the generator to obtain a nucleus image that is visually more similar to the real image. Finally, the segmented image output by the FCN model is used as the input of the GAN network to achieve high-precision segmentation of the nucleus image. The proposed method is experimentally demonstrated based on the 2018 data science bowl dataset. The results show that it can achieve rapid convergence, and the mean intersection over union (MIoU) is 85.34%, which is better than other comparison methods.

scholarly journals Multimodal Human Recognition in Significantly Low Illumination Environment Using Modified EnlightenGAN

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1934
Author(s):  
Ja Hyung Koo ◽  
Se Woon Cho ◽  
Na Rae Baek ◽  
Kang Ryoung Park
Keyword(s):  
Identification Accuracy ◽  
Indoor Environments ◽  
Generative Adversarial Network ◽  
Score Level Fusion ◽  
Human Recognition ◽  
Adversarial Network ◽  
The Face ◽  
Low Illumination ◽  
Camera Sensors ◽  
Better Than

Human recognition in indoor environments occurs both during the day and at night. During the day, human recognition encounters performance degradation owing to a blur generated when a camera captures a person’s image. However, when images are captured at night with a camera, it is difficult to obtain perfect images of a person without light, and the input images are very noisy owing to the properties of camera sensors in low-illumination environments. Studies have been conducted in the past on face recognition in low-illumination environments; however, there is lack of research on face- and body-based human recognition in very low illumination environments. To solve these problems, this study proposes a modified enlighten generative adversarial network (modified EnlightenGAN) in which a very low illumination image is converted to a normal illumination image, and the matching scores of deep convolutional neural network (CNN) features of the face and body in the converted image are combined with a score-level fusion for recognition. The two types of databases used in this study are the Dongguk face and body database version 3 (DFB-DB3) and the ChokePoint open dataset. The results of the experiment conducted using the two databases show that the human verification accuracy (equal error rate (ERR)) and identification accuracy (rank 1 genuine acceptance rate (GAR)) of the proposed method were 7.291% and 92.67% for DFB-DB3 and 10.59% and 87.78% for the ChokePoint dataset, respectively. Accordingly, the performance of the proposed method was better than the previous methods.

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