Connectivity-informed drainage network generation using deep convolution generative adversarial networks

AbstractStochastic network modeling is often limited by high computational costs to generate a large number of networks enough for meaningful statistical evaluation. In this study, Deep Convolutional Generative Adversarial Networks (DCGANs) were applied to quickly reproduce drainage networks from the already generated network samples without repetitive long modeling of the stochastic network model, Gibb’s model. In particular, we developed a novel connectivity-informed method that converts the drainage network images to the directional information of flow on each node of the drainage network, and then transforms it into multiple binary layers where the connectivity constraints between nodes in the drainage network are stored. DCGANs trained with three different types of training samples were compared; (1) original drainage network images, (2) their corresponding directional information only, and (3) the connectivity-informed directional information. A comparison of generated images demonstrated that the novel connectivity-informed method outperformed the other two methods by training DCGANs more effectively and better reproducing accurate drainage networks due to its compact representation of the network complexity and connectivity. This work highlights that DCGANs can be applicable for high contrast images common in earth and material sciences where the network, fractures, and other high contrast features are important.

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Intrusion detection of railway clearance from infrared images using generative adversarial networks

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-192141 ◽

2020 ◽

pp. 1-13

Author(s):

Yundong Li ◽

Yi Liu ◽

Han Dong ◽

Wei Hu ◽

Chen Lin

Keyword(s):

Intrusion Detection ◽

Synthetic Data ◽

Generative Adversarial Networks ◽

Generation Model ◽

Single Shot ◽

Data Generation ◽

Infrared Images ◽

Adversarial Networks ◽

Training Samples ◽

Rgb Images

The intrusion detection of railway clearance is crucial for avoiding railway accidents caused by the invasion of abnormal objects, such as pedestrians, falling rocks, and animals. However, detecting intrusions using deep learning methods from infrared images captured at night remains a challenging task because of the lack of sufficient training samples. To address this issue, a transfer strategy that migrates daytime RGB images to the nighttime style of infrared images is proposed in this study. The proposed method consists of two stages. In the first stage, a data generation model is trained on the basis of generative adversarial networks using RGB images and a small number of infrared images, and then, synthetic samples are generated using a well-trained model. In the second stage, a single shot multibox detector (SSD) model is trained using synthetic data and utilized to detect abnormal objects from infrared images at nighttime. To validate the effectiveness of the proposed method, two groups of experiments, namely, railway and non-railway scenes, are conducted. Experimental results demonstrate the effectiveness of the proposed method, and an improvement of 17.8% is achieved for object detection at nighttime.

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Usefulness of a Metal Artifact Reduction Algorithm in Digital Tomosynthesis Using a Combination of Hybrid Generative Adversarial Networks

Diagnostics ◽

10.3390/diagnostics11091629 ◽

2021 ◽

Vol 11 (9) ◽

pp. 1629

Author(s):

Tsutomu Gomi ◽

Rina Sakai ◽

Hidetake Hara ◽

Yusuke Watanabe ◽

Shinya Mizukami

Keyword(s):

Radiation Dose ◽

Dose Reduction ◽

Gumbel Distribution ◽

Radiation Dose Reduction ◽

Generative Adversarial Networks ◽

Artifact Reduction ◽

Metal Artifact ◽

Digital Tomosynthesis ◽

The Novel ◽

Adversarial Networks

In this study, a novel combination of hybrid generative adversarial networks (GANs) comprising cycle-consistent GAN, pix2pix, and (mask pyramid network) MPN (CGpM-metal artifact reduction [MAR]), was developed using projection data to reduce metal artifacts and the radiation dose during digital tomosynthesis. The CGpM-MAR algorithm was compared with the conventional filtered back projection (FBP) without MAR, FBP with MAR, and convolutional neural network MAR. The MAR rates were compared using the artifact index (AI) and Gumbel distribution of the largest variation analysis using a prosthesis phantom at various radiation doses. The novel CGpM-MAR yielded an adequately effective overall performance in terms of AI. The resulting images yielded good results independently of the type of metal used in the prosthesis phantom (p < 0.05) and good artifact removal at 55% radiation-dose reduction. Furthermore, the CGpM-MAR represented the minimum in the model with the largest variation at 55% radiation-dose reduction. Regarding the AI and Gumbel distribution analysis, the novel CGpM-MAR yielded superior MAR when compared with the conventional reconstruction algorithms with and without MAR at 55% radiation-dose reduction and presented features most similar to the reference FBP. CGpM-MAR presents a promising method for metal artifact and radiation-dose reduction in clinical practice.

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Harnessing GANs for Zero-Shot Learning of New Classes in Visual Speech Recognition

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i03.5649 ◽

2020 ◽

Vol 34 (03) ◽

pp. 2645-2652 ◽

Cited By ~ 2

Author(s):

Yaman Kumar ◽

Dhruva Sahrawat ◽

Shubham Maheshwari ◽

Debanjan Mahata ◽

Amanda Stent ◽

...

Keyword(s):

Speech Recognition ◽

Classification Problem ◽

Visual Speech ◽

Training Data ◽

Generative Adversarial Networks ◽

Adversarial Networks ◽

Novel Approach ◽

Visual Speech Recognition ◽

Training Samples ◽

English Training

Visual Speech Recognition (VSR) is the process of recognizing or interpreting speech by watching the lip movements of the speaker. Recent machine learning based approaches model VSR as a classification problem; however, the scarcity of training data leads to error-prone systems with very low accuracies in predicting unseen classes. To solve this problem, we present a novel approach to zero-shot learning by generating new classes using Generative Adversarial Networks (GANs), and show how the addition of unseen class samples increases the accuracy of a VSR system by a significant margin of 27% and allows it to handle speaker-independent out-of-vocabulary phrases. We also show that our models are language agnostic and therefore capable of seamlessly generating, using English training data, videos for a new language (Hindi). To the best of our knowledge, this is the first work to show empirical evidence of the use of GANs for generating training samples of unseen classes in the domain of VSR, hence facilitating zero-shot learning. We make the added videos for new classes publicly available along with our code1.

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Shale Digital Core Image Generation Based on Generative Adversarial Networks

Journal of Energy Resources Technology ◽

10.1115/1.4048052 ◽

2020 ◽

Vol 143 (3) ◽

Cited By ~ 1

Author(s):

Wenshu Zha ◽

Xingbao Li ◽

Daolun Li ◽

Yan Xing ◽

Lei He ◽

...

Keyword(s):

Organic Matter ◽

Morphological Characteristics ◽

Generative Adversarial Networks ◽

Reconstruction Method ◽

Core Samples ◽

Adversarial Networks ◽

Training Samples ◽

Digital Core ◽

Artificial Intelligence Models ◽

Convolution Properties

Abstract Stochastic reconstruction of digital core images is a vital part of digital core physics analysis, aiming to generate representative microstructure samples for sampling and uncertainty quantification analysis. This paper proposes a novel reconstruction method of the digital core of shale based on generative adversarial networks (GANs) with powerful capabilities of the generation of samples. GANs are a series of unsupervised generative artificial intelligence models that take the noise vector as an input. In this paper, the GANs with a generative and a discriminative network are created respectively, and the shale image with 45 nm/pixel preprocessed by the three-value-segmentation method is used as training samples. The generative network is used to learn the distribution of real training samples, and the discriminative network is used to distinguish real samples from synthetic ones. Finally, realistic digital core samples of shale are successfully reconstructed through the adversarial training process. We used the Fréchet inception distance (FID) and Kernel inception distance (KID) to evaluate the ability of GANs to generate real digital core samples of shale. The comparison of the morphological characteristics between them, such as the ratio of organic matter and specific surface area of organic matter, indicates that real and reconstructed samples are highly close. The results show that deep convolutional generative adversarial networks with full convolution properties can reconstruct digital core samples of shale effectively. Therefore, compared with the classical methods of reconstruction, the new reconstruction method is more promising.

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Blind Image Separation Method Based on Cascade Generative Adversarial Networks

Applied Sciences ◽

10.3390/app11209416 ◽

2021 ◽

Vol 11 (20) ◽

pp. 9416

Author(s):

Fei Jia ◽

Jindong Xu ◽

Xiao Sun ◽

Yongli Ma ◽

Mengying Ni

Keyword(s):

Prior Knowledge ◽

Image Synthesis ◽

Generative Adversarial Networks ◽

Synthetic Image ◽

Adversarial Networks ◽

Training Samples ◽

Image Separation ◽

Blind Image ◽

Insufficient Number ◽

Image Datasets

To solve the challenge of single-channel blind image separation (BIS) caused by unknown prior knowledge during the separation process, we propose a BIS method based on cascaded generative adversarial networks (GANs). To ensure that the proposed method can perform well in different scenarios and to address the problem of an insufficient number of training samples, a synthetic network is added to the separation network. This method is composed of two GANs: a U-shaped GAN (UGAN), which is used to learn image synthesis, and a pixel-to-attention GAN (PAGAN), which is used to learn image separation. The two networks jointly complete the task of image separation. UGAN uses the unpaired mixed image and the unmixed image to learn the mixing style, thereby generating an image with the “true” mixing characteristics which addresses the problem of an insufficient number of training samples for the PAGAN. A self-attention mechanism is added to the PAGAN to quickly extract important features from the image data. The experimental results show that the proposed method achieves good results on both synthetic image datasets and real remote sensing image datasets. Moreover, it can be used for image separation in different scenarios which lack prior knowledge and training samples.

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Investigating the Performance of Generative Adversarial Networks for Prostate Tissue Detection and Segmentation

Journal of Imaging ◽

10.3390/jimaging6090083 ◽

2020 ◽

Vol 6 (9) ◽

pp. 83 ◽

Cited By ~ 1

Author(s):

Ufuk Cem Birbiri ◽

Azam Hamidinekoo ◽

Amélie Grall ◽

Paul Malcolm ◽

Reyer Zwiggelaar

Keyword(s):

Region Of Interest ◽

Training Data ◽

Prostate Tissue ◽

Generative Adversarial Networks ◽

Correct Identification ◽

Adversarial Networks ◽

Training Samples ◽

Mri Scans ◽

Magnetic Resonance Imaging Mri ◽

Public Datasets

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-proven prostate cancer. Due to the limited amount of available training data, three augmentation schemes were proposed to artificially increase the training samples. These models were tested on a clinical dataset annotated for this study and on a public dataset (PROMISE12). The cGAN model outperformed the U-Net and cycleGAN predictions owing to the inclusion of paired image supervision. Based on our quantitative results, cGAN gained a Dice score of 0.78 and 0.75 on the private and the PROMISE12 public datasets, respectively.

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Automated Segmentation of Epithelial Tissue Using Cycle-Consistent Generative Adversarial Networks

10.1101/311373 ◽

2018 ◽

Cited By ~ 6

Author(s):

Matthias Häring ◽

Jörg Großhans ◽

Fred Wolf ◽

Stephan Eule

Keyword(s):

Ground Truth ◽

Training Data ◽

Generative Adversarial Networks ◽

Epithelial Tissue ◽

Automated Segmentation ◽

Segmentation Method ◽

Adversarial Networks ◽

Training Samples ◽

Segmentation Of Images ◽

Image Mask

AbstractA central problem in biomedical imaging is the automated segmentation of images for further quantitative analysis. Recently, fully convolutional neural networks, such as the U-Net, were applied successfully in a variety of segmentation tasks. A downside of this approach is the requirement for a large amount of well-prepared training samples, consisting of image - ground truth mask pairs. Since training data must be created by hand for each experiment, this task can be very costly and time-consuming. Here, we present a segmentation method based on cycle consistent generative adversarial networks, which can be trained even in absence of prepared image - mask pairs. We show that it successfully performs image segmentation tasks on samples with substantial defects and even generalizes well to different tissue types.

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The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN

Sensors ◽

10.3390/s19143145 ◽

2019 ◽

Vol 19 (14) ◽

pp. 3145 ◽

Cited By ~ 9

Author(s):

Yuantao Chen ◽

Jiajun Tao ◽

Jin Wang ◽

Xi Chen ◽

Jingbo Xie ◽

...

Keyword(s):

Sensor Network ◽

Loss Function ◽

Network Structure ◽

Posterior Probability ◽

Feature Matching ◽

Generative Adversarial Networks ◽

Classification Algorithms ◽

The Novel ◽

The Real ◽

Adversarial Networks

To address the problem of unstable training and poor accuracy in image classification algorithms based on generative adversarial networks (GAN), a novel sensor network structure for classification processing using auxiliary classifier generative adversarial networks (ACGAN) is proposed in this paper. Firstly, the real/fake discrimination of sensor samples in the network has been canceled at the output layer of the discriminative network and only the posterior probability estimation of the sample tag is outputted. Secondly, by regarding the real sensor samples as supervised data and the generative sensor samples as labeled fake data, we have reconstructed the loss function of the generator and discriminator by using the real/fake attributes of sensor samples and the cross-entropy loss function of the label. Thirdly, the pooling and caching method has been introduced into the discriminator to enable more effective extraction of the classification features. Finally, feature matching has been added to the discriminative network to ensure the diversity of the generative sensor samples. Experimental results have shown that the proposed algorithm (CP-ACGAN) achieves better classification accuracy on the MNIST dataset, CIFAR10 dataset and CIFAR100 dataset than other solutions. Moreover, when compared with the ACGAN and CNN classification algorithms, which have the same deep network structure as CP-ACGAN, the proposed method continues to achieve better classification effects and stability than other main existing sensor solutions.

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Data Augmentation for EEG-Based Emotion Recognition Using Generative Adversarial Networks

Frontiers in Computational Neuroscience ◽

10.3389/fncom.2021.723843 ◽

2021 ◽

Vol 15 ◽

Author(s):

Guangcheng Bao ◽

Bin Yan ◽

Li Tong ◽

Jun Shu ◽

Linyuan Wang ◽

...

Keyword(s):

Emotion Recognition ◽

Data Augmentation ◽

Generative Models ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Training Samples ◽

Using Data ◽

Effective Models

One of the greatest limitations in the field of EEG-based emotion recognition is the lack of training samples, which makes it difficult to establish effective models for emotion recognition. Inspired by the excellent achievements of generative models in image processing, we propose a data augmentation model named VAE-D2GAN for EEG-based emotion recognition using a generative adversarial network. EEG features representing different emotions are extracted as topological maps of differential entropy (DE) under five classical frequency bands. The proposed model is designed to learn the distributions of these features for real EEG signals and generate artificial samples for training. The variational auto-encoder (VAE) architecture can learn the spatial distribution of the actual data through a latent vector, and is introduced into the dual discriminator GAN to improve the diversity of the generated artificial samples. To evaluate the performance of this model, we conduct a systematic test on two public emotion EEG datasets, the SEED and the SEED-IV. The obtained recognition accuracy of the method using data augmentation shows as 92.5 and 82.3%, respectively, on the SEED and SEED-IV datasets, which is 1.5 and 3.5% higher than that of methods without using data augmentation. The experimental results show that the artificial samples generated by our model can effectively enhance the performance of the EEG-based emotion recognition.

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GANDaLF: GAN for Data-Limited Fingerprinting

Proceedings on Privacy Enhancing Technologies ◽

10.2478/popets-2021-0029 ◽

2021 ◽

Vol 2021 (2) ◽

pp. 305-322

Author(s):

Se Eun Oh ◽

Nate Mathews ◽

Mohammad Saidur Rahman ◽

Matthew Wright ◽

Nicholas Hopper

Keyword(s):

Deep Learning ◽

Training Data ◽

Generative Adversarial Networks ◽

Large Set ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Closed World ◽

Training Samples ◽

A Site

Abstract We introduce Generative Adversarial Networks for Data-Limited Fingerprinting (GANDaLF), a new deep-learning-based technique to perform Website Fingerprinting (WF) on Tor traffic. In contrast to most earlier work on deep-learning for WF, GANDaLF is intended to work with few training samples, and achieves this goal through the use of a Generative Adversarial Network to generate a large set of “fake” data that helps to train a deep neural network in distinguishing between classes of actual training data. We evaluate GANDaLF in low-data scenarios including as few as 10 training instances per site, and in multiple settings, including fingerprinting of website index pages and fingerprinting of non-index pages within a site. GANDaLF achieves closed-world accuracy of 87% with just 20 instances per site (and 100 sites) in standard WF settings. In particular, GANDaLF can outperform Var-CNN and Triplet Fingerprinting (TF) across all settings in subpage fingerprinting. For example, GANDaLF outperforms TF by a 29% margin and Var-CNN by 38% for training sets using 20 instances per site.

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