Nonlinear optimization generating the Tomb Mural Blocks by GANS

AbstractTomb murals are the special kind of murals that are buried underground. Due to the narrow exit of the tomb passage, the tomb murals were excavated by dividing the whole mural into blocks, which made lots of information missing between the blocks. The digital restoration technology Image inpainting uses the edge information around the missing parts to spread the information inside of the defect area and fills the information from the outside to the inside. But it is not suitable for filling the missing parts between the tomb mural blocks. Because these parts are large for exemplar-based inpainting which may make texture dislocation and for PDE which may make cartoon blur. It is a need to generate the information outwards to complete the information. The generative adversarial network uses deep learning training by the murals remains to generate the information from inside to outside, but the typical GAN doesn‘t have a good nonlinear feature. This paper provided a generating technology based on the deep convolution generative adversarial network to rebuild the missing information between the tomb mural blocks. It built the training data set of the simulation platform with Keras and designed a whole mural generation scheme based on DCGAN. In order to get better generated results to avoid the bad artifacts; it adds the nonlinear layers by choosing 13 layers convolution and 2 deconvolution layers of the generator and contained 5 layers convolution discriminator; it designed a new phased nonlinear loss function by using Pycharm pretreatment for Numpy array file data sets; finally, it completed the generate tomb mural information to obtain the good simulation effect.

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Underwater Acoustic Target Recognition Based on Generative Adversarial Network Data Augmentation

INTER-NOISE and NOISE-CON Congress and Conference Proceedings ◽

10.3397/in-2021-2737 ◽

2021 ◽

Vol 263 (2) ◽

pp. 4558-4564

Author(s):

Minghong Zhang ◽

Xinwei Luo

Keyword(s):

Data Augmentation ◽

Target Recognition ◽

Training Data ◽

Small Samples ◽

Generative Adversarial Network ◽

Data Set ◽

Underwater Acoustic ◽

Adversarial Network ◽

Acoustic Target ◽

The Impact

Underwater acoustic target recognition is an important aspect of underwater acoustic research. In recent years, machine learning has been developed continuously, which is widely and effectively applied in underwater acoustic target recognition. In order to acquire good recognition results and reduce the problem of overfitting, Adequate data sets are essential. However, underwater acoustic samples are relatively rare, which has a certain impact on recognition accuracy. In this paper, in addition of the traditional audio data augmentation method, a new method of data augmentation using generative adversarial network is proposed, which uses generator and discriminator to learn the characteristics of underwater acoustic samples, so as to generate reliable underwater acoustic signals to expand the training data set. The expanded data set is input into the deep neural network, and the transfer learning method is applied to further reduce the impact caused by small samples by fixing part of the pre-trained parameters. The experimental results show that the recognition result of this method is better than the general underwater acoustic recognition method, and the effectiveness of this method is verified.

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GAINESIS: Generative Artificial Intelligence NEtlists SynthesIS

Electronics ◽

10.3390/electronics11020245 ◽

2022 ◽

Vol 11 (2) ◽

pp. 245

Author(s):

Konstantinos G. Liakos ◽

Georgios K. Georgakilas ◽

Fotis C. Plessas ◽

Paris Kitsos

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Power Analysis ◽

Public Libraries ◽

Data Sets ◽

Hardware Trojan ◽

Generative Adversarial Network ◽

Data Set ◽

Encrypted Data ◽

Adversarial Network

A significant problem in the field of hardware security consists of hardware trojan (HT) viruses. The insertion of HTs into a circuit can be applied for each phase of the circuit chain of production. HTs degrade the infected circuit, destroy it or leak encrypted data. Nowadays, efforts are being made to address HTs through machine learning (ML) techniques, mainly for the gate-level netlist (GLN) phase, but there are some restrictions. Specifically, the number and variety of normal and infected circuits that exist through the free public libraries, such as Trust-HUB, are based on the few samples of benchmarks that have been created from circuits large in size. Thus, it is difficult, based on these data, to develop robust ML-based models against HTs. In this paper, we propose a new deep learning (DL) tool named Generative Artificial Intelligence Netlists SynthesIS (GAINESIS). GAINESIS is based on the Wasserstein Conditional Generative Adversarial Network (WCGAN) algorithm and area–power analysis features from the GLN phase and synthesizes new normal and infected circuit samples for this phase. Based on our GAINESIS tool, we synthesized new data sets, different in size, and developed and compared seven ML classifiers. The results demonstrate that our new generated data sets significantly enhance the performance of ML classifiers compared with the initial data set of Trust-HUB.

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Feature Learning for SAR Target Recognition with Unknown Classes by Using CVAE-GAN

Remote Sensing ◽

10.3390/rs13183554 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3554

Author(s):

Xiaowei Hu ◽

Weike Feng ◽

Yiduo Guo ◽

Qiang Wang

Keyword(s):

Target Recognition ◽

Feature Learning ◽

Feature Space ◽

Automatic Target Recognition ◽

Data Sets ◽

Generative Adversarial Network ◽

Data Set ◽

Adversarial Network ◽

Public Data ◽

Class Labels

Even though deep learning (DL) has achieved excellent results on some public data sets for synthetic aperture radar (SAR) automatic target recognition(ATR), several problems exist at present. One is the lack of transparency and interpretability for most of the existing DL networks. Another is the neglect of unknown target classes which are often present in practice. To solve the above problems, a deep generation as well as recognition model is derived based on Conditional Variational Auto-encoder (CVAE) and Generative Adversarial Network (GAN). A feature space for SAR-ATR is built based on the proposed CVAE-GAN model. By using the feature space, clear SAR images can be generated with given class labels and observation angles. Besides, the feature of the SAR image is continuous in the feature space and can represent some attributes of the target. Furthermore, it is possible to classify the known classes and reject the unknown target classes by using the feature space. Experiments on the MSTAR data set validate the advantages of the proposed method.

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3D Design Using Generative Adversarial Networks and Physics-Based Validation

Journal of Mechanical Design ◽

10.1115/1.4045419 ◽

2019 ◽

Vol 142 (7) ◽

Cited By ~ 2

Author(s):

Dule Shu ◽

James Cunningham ◽

Gary Stump ◽

Simon W. Miller ◽

Michael A. Yukish ◽

...

Keyword(s):

Evaluation Process ◽

3D Models ◽

Training Data ◽

Generative Adversarial Networks ◽

3D Design ◽

Generative Adversarial Network ◽

Data Set ◽

High Performing ◽

Adversarial Network

Abstract The authors present a generative adversarial network (GAN) model that demonstrates how to generate 3D models in their native format so that they can be either evaluated using complex simulation environments or realized using methods such as additive manufacturing. Once initially trained, the GAN can create additional training data itself by generating new designs, evaluating them in a physics-based virtual environment, and adding the high performing ones to the training set. A case study involving a GAN model that is initially trained on 4045 3D aircraft models is used for demonstration, where a training data set that has been updated with GAN-generated and evaluated designs results in enhanced model generation, in both the geometric feasibility and performance of the designs. Z-tests on the performance scores of the generated aircraft models indicate a statistically significant improvement in the functionality of the generated models after three iterations of the training-evaluation process. In the case study, a number of techniques are explored to structure the generate-evaluate process in order to balance the need to generate feasible designs with the need for innovative designs.

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Generative adversarial network based on chaotic time series

Scientific Reports ◽

10.1038/s41598-019-49397-2 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 3

Author(s):

Makoto Naruse ◽

Takashi Matsubara ◽

Nicolas Chauvet ◽

Kazutaka Kanno ◽

Tianyu Yang ◽

...

Keyword(s):

Time Series ◽

Semiconductor Lasers ◽

Latent Variables ◽

Chaotic Time Series ◽

Training Data ◽

Generative Adversarial Networks ◽

Data Sets ◽

Deep Convolutional Neural Networks ◽

Generative Adversarial Network ◽

Adversarial Network

Abstract Generative adversarial networks (GANs) are becoming increasingly important in the artificial construction of natural images and related functionalities, wherein two types of networks called generators and discriminators evolve through adversarial mechanisms. Using deep convolutional neural networks and related techniques, high-resolution and highly realistic scenes, human faces, etc. have been generated. GANs generally require large amounts of genuine training data sets, as well as vast amounts of pseudorandom numbers. In this study, we utilized chaotic time series generated experimentally by semiconductor lasers for the latent variables of a GAN, whereby the inherent nature of chaos could be reflected or transformed into the generated output data. We show that the similarity in proximity, which describes the robustness of the generated images with respect to minute changes in the input latent variables, is enhanced, while the versatility overall is not severely degraded. Furthermore, we demonstrate that the surrogate chaos time series eliminates the signature of the generated images that is originally observed corresponding to the negative autocorrelation inherent in the chaos sequence. We also address the effects of utilizing chaotic time series to retrieve images from the trained generator.

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Tomographic reconstruction with a generative adversarial network

Journal of Synchrotron Radiation ◽

10.1107/s1600577520000831 ◽

2020 ◽

Vol 27 (2) ◽

pp. 486-493 ◽

Cited By ~ 2

Author(s):

Xiaogang Yang ◽

Maik Kahnt ◽

Dennis Brückner ◽

Andreas Schropp ◽

Yakub Fam ◽

...

Keyword(s):

Phase Retrieval ◽

Learning Algorithm ◽

Tomographic Reconstruction ◽

Training Data ◽

Training Procedure ◽

Contrast Imaging ◽

Generative Adversarial Network ◽

Data Set ◽

Adversarial Network ◽

Deep Learning Algorithm

This paper presents a deep learning algorithm for tomographic reconstruction (GANrec). The algorithm uses a generative adversarial network (GAN) to solve the inverse of the Radon transform directly. It works for independent sinograms without additional training steps. The GAN has been developed to fit the input sinogram with the model sinogram generated from the predicted reconstruction. Good quality reconstructions can be obtained during the minimization of the fitting errors. The reconstruction is a self-training procedure based on the physics model, instead of on training data. The algorithm showed significant improvements in the reconstruction accuracy, especially for missing-wedge tomography acquired at less than 180° rotational range. It was also validated by reconstructing a missing-wedge X-ray ptychographic tomography (PXCT) data set of a macroporous zeolite particle, for which only 51 projections over 70° could be collected. The GANrec recovered the 3D pore structure with reasonable quality for further analysis. This reconstruction concept can work universally for most of the ill-posed inverse problems if the forward model is well defined, such as phase retrieval of in-line phase-contrast imaging.

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A deep learning segmentation strategy that minimizes the amount of manually annotated images

F1000Research ◽

10.12688/f1000research.52026.1 ◽

2021 ◽

Vol 10 ◽

pp. 256

Author(s):

Thierry Pécot ◽

Alexander Alekseyenko ◽

Kristin Wallace

Keyword(s):

Deep Learning ◽

Data Augmentation ◽

Training Data ◽

Artificial Data ◽

Deep Convolutional Neural Networks ◽

Generative Adversarial Network ◽

Data Set ◽

Segmentation Strategy ◽

Adversarial Network ◽

The Impact

Deep learning has revolutionized the automatic processing of images. While deep convolutional neural networks have demonstrated astonishing segmentation results for many biological objects acquired with microscopy, this technology's good performance relies on large training datasets. In this paper, we present a strategy to minimize the amount of time spent in manually annotating images for segmentation. It involves using an efficient and open source annotation tool, the artificial increase of the training data set with data augmentation, the creation of an artificial data set with a conditional generative adversarial network and the combination of semantic and instance segmentations. We evaluate the impact of each of these approaches for the segmentation of nuclei in 2D widefield images of human precancerous polyp biopsies in order to define an optimal strategy.

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Optimization of Cardiac Magnetic Resonance Synthetic Image Based on Simulated Generative Adversarial Network

Mathematical Problems in Engineering ◽

10.1155/2021/3279563 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Ying Fu ◽

MinXue Gong ◽

Guang Yang ◽

JinRong Hu ◽

Hong Wei ◽

...

Keyword(s):

Cardiac Magnetic Resonance ◽

Magnetic Resonance ◽

Data Augmentation ◽

Small Sample ◽

Training Data ◽

Synthetic Image ◽

Generative Adversarial Network ◽

Data Set ◽

Adversarial Network ◽

Convolution Model

The generative adversarial network (GAN) has advantage to fit data distribution, so it can achieve data augmentation by fitting the real distribution and synthesizing additional training data. In this way, the deep convolution model can also be well trained in the case of using a small sample medical image data set. However, some certain gaps still exist between synthetic images and real images. In order to further narrow those gaps, this paper proposed a method that applies SimGAN on cardiac magnetic resonance synthetic image optimization task. Meanwhile, the improved residual structure is used to deepen the network structure to improve the performance of the optimizer. Lastly, the experiments will show the good result of our data augmentation method based on GAN.

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Exploration of Campus Layout Based on Generative Adversarial Network

Proceedings of the 2020 DigitalFUTURES ◽

10.1007/978-981-33-4400-6_16 ◽

2021 ◽

pp. 169-178

Author(s):

Yubo Liu ◽

Yihua Luo ◽

Qiaoming Deng ◽

Xuanxing Zhou

Keyword(s):

Deep Learning ◽

Experimental Results ◽

Data Sets ◽

Generative Adversarial Network ◽

Data Set ◽

Effective Screening ◽

Adversarial Network ◽

Sample Data ◽

Layout Generation ◽

The Given

AbstractThis paper aims to explore the idea and method of using deep learning with a small amount sample to realize campus layout generation. From the perspective of the architect, we construct two small amount sample campus layout data sets through artificial screening with the preference of the specific architects. These data sets are used to train the ability of Pix2Pix model to automatically generate the campus layout under the condition of the given campus boundary and surrounding roads. Through the analysis of the experimental results, this paper finds that under the premise of effective screening of the collected samples, even using a small amount sample data set for deep learning can achieve a good result.

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Anomaly detection in Hyper Suprime-Cam galaxy images with generative adversarial networks

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stab2589 ◽

2021 ◽

Author(s):

Kate Storey-Fisher ◽

Marc Huertas-Company ◽

Nesar Ramachandra ◽

Francois Lanusse ◽

Alexie Leauthaud ◽

...

Keyword(s):

Anomaly Detection ◽

Dwarf Galaxy ◽

Generative Adversarial Networks ◽

Data Sets ◽

Galaxy Mergers ◽

Generative Adversarial Network ◽

Data Set ◽

Star Forming ◽

Adversarial Network ◽

Astronomical Surveys

Abstract The problem of anomaly detection in astronomical surveys is becoming increasingly important as data sets grow in size. We present the results of an unsupervised anomaly detection method using a Wasserstein generative adversarial network (WGAN) on nearly one million optical galaxy images in the Hyper Suprime-Cam (HSC) survey. The WGAN learns to generate realistic HSC-like galaxies that follow the distribution of the data set; anomalous images are defined based on a poor reconstruction by the generator and outlying features learned by the discriminator. We find that the discriminator is more attuned to potentially interesting anomalies compared to the generator, and compared to a simpler autoencoder-based anomaly detection approach, so we use the discriminator-selected images to construct a high-anomaly sample of ∼13 000 objects. We propose a new approach to further characterize these anomalous images: we use a convolutional autoencoder to reduce the dimensionality of the residual differences between the real and WGAN-reconstructed images and perform UMAP clustering on these. We report detected anomalies of interest including galaxy mergers, tidal features, and extreme star-forming galaxies. A follow-up spectroscopic analysis of one of these anomalies is detailed in the Appendix; we find that it is an unusual system most likely to be a metal-poor dwarf galaxy with an extremely blue, higher-metallicity H ii region. We have released a catalog with the WGAN anomaly scores; the code and catalog are available at https://github.com/kstoreyf/anomalies-GAN-HSC, and our interactive visualization tool for exploring the clustered data is at https://weirdgalaxi.es.

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