scholarly journals Post-Disaster Building Damage Detection from Earth Observation Imagery Using Unsupervised and Transferable Anomaly Detecting Generative Adversarial Networks

2020 ◽  
Vol 12 (24) ◽  
pp. 4193
Author(s):  
Sofia Tilon ◽  
Francesco Nex ◽  
Norman Kerle ◽  
George Vosselman
Keyword(s):  
Deep Learning ◽  
Damage Detection ◽  
Satellite Imagery ◽  
Building Damage ◽  
Training Data ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Custom Made ◽  
Post Disaster

We present an unsupervised deep learning approach for post-disaster building damage detection that can transfer to different typologies of damage or geographical locations. Previous advances in this direction were limited by insufficient qualitative training data. We propose to use a state-of-the-art Anomaly Detecting Generative Adversarial Network (ADGAN) because it only requires pre-event imagery of buildings in their undamaged state. This approach aids the post-disaster response phase because the model can be developed in the pre-event phase and rapidly deployed in the post-event phase. We used the xBD dataset, containing pre- and post- event satellite imagery of several disaster-types, and a custom made Unmanned Aerial Vehicle (UAV) dataset, containing post-earthquake imagery. Results showed that models trained on UAV-imagery were capable of detecting earthquake-induced damage. The best performing model for European locations obtained a recall, precision and F1-score of 0.59, 0.97 and 0.74, respectively. Models trained on satellite imagery were capable of detecting damage on the condition that the training dataset was void of vegetation and shadows. In this manner, the best performing model for (wild)fire events yielded a recall, precision and F1-score of 0.78, 0.99 and 0.87, respectively. Compared to other supervised and/or multi-epoch approaches, our results are encouraging. Moreover, in addition to image classifications, we show how contextual information can be used to create detailed damage maps without the need of a dedicated multi-task deep learning framework. Finally, we formulate practical guidelines to apply this single-epoch and unsupervised method to real-world applications.

scholarly journals INFRASTRUCTURE DEGRADATION AND POST-DISASTER DAMAGE DETECTION USING ANOMALY DETECTING GENERATIVE ADVERSARIAL NETWORKS

2020 ◽  
Vol V-2-2020 ◽  
pp. 573-582 ◽  
Author(s):  
S. M. Tilon ◽  
F. Nex ◽  
D. Duarte ◽  
N. Kerle ◽  
G. Vosselman
Keyword(s):  
Deep Learning ◽  
Damage Detection ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Essential Information ◽  
Urban Scenes ◽  
Adversarial Network ◽  
Collapsed Buildings ◽  
Post Disaster

Abstract. Degradation and damage detection provides essential information to maintenance workers in routine monitoring and to first responders in post-disaster scenarios. Despite advance in Earth Observation (EO), image analysis and deep learning techniques, the quality and quantity of training data for deep learning is still limited. As a result, no robust method has been found yet that can transfer and generalize well over a variety of geographic locations and typologies of damages. Since damages can be seen as anomalies, occurring sparingly over time and space, we propose to use an anomaly detecting Generative Adversarial Network (GAN) to detect damages. The main advantages of using GANs are that only healthy unannotated images are needed, and that a variety of damages, including the never before seen damage, can be detected. In this study we aimed to investigate 1) the ability of anomaly detecting GANs to detect degradation (potholes and cracks) in asphalt road infrastructures using Mobile Mapper imagery and building damage (collapsed buildings, rubble piles) using post-disaster aerial imagery, and 2) the sensitivity of this method against various types of pre-processing. Our results show that we can detect damages in urban scenes at satisfying levels but not on asphalt roads. Future work will investigate how to further classify the found damages and how to improve damage detection for asphalt roads.

GANDaLF: GAN for Data-Limited Fingerprinting

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.

scholarly journals Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4953
Author(s):  
Sara Al-Emadi ◽  
Abdulla Al-Ali ◽  
Abdulaziz Al-Ali
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Learning Algorithms ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Networks ◽  
Detection And Identification ◽  
Learning Techniques ◽  
The Impact

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones.

scholarly journals Deep Learning-Based Computer-Aided Diagnosis System for Gastroscopy Image Classification Using Synthetic Data

2021 ◽  
Vol 11 (2) ◽  
pp. 760
Author(s):  
Yun-ji Kim ◽  
Hyun Chin Cho ◽  
Hyun-chong Cho
Keyword(s):  
Deep Learning ◽  
Disease Diagnosis ◽  
Computer Aided Diagnosis ◽  
Medical Data ◽  
Classification Model ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Original Training ◽  
Computer Aided ◽  
Aided Diagnosis

Gastric cancer has a high mortality rate worldwide, but it can be prevented with early detection through regular gastroscopy. Herein, we propose a deep learning-based computer-aided diagnosis (CADx) system applying data augmentation to help doctors classify gastroscopy images as normal or abnormal. To improve the performance of deep learning, a large amount of training data are required. However, the collection of medical data, owing to their nature, is highly expensive and time consuming. Therefore, data were generated through deep convolutional generative adversarial networks (DCGAN), and 25 augmentation policies optimized for the CIFAR-10 dataset were implemented through AutoAugment to augment the data. Accordingly, a gastroscopy image was augmented, only high-quality images were selected through an image quality-measurement method, and gastroscopy images were classified as normal or abnormal through the Xception network. We compared the performances of the original training dataset, which did not improve, the dataset generated through the DCGAN, the dataset augmented through the augmentation policies of CIFAR-10, and the dataset combining the two methods. The dataset combining the two methods delivered the best performance in terms of accuracy (0.851) and achieved an improvement of 0.06 over the original training dataset. We confirmed that augmenting data through the DCGAN and CIFAR-10 augmentation policies is most suitable for the classification model for normal and abnormal gastric endoscopy images. The proposed method not only solves the medical-data problem but also improves the accuracy of gastric disease diagnosis.

Linear electromagnetic inverse scattering via generative adversarial networks

2021 ◽  
pp. 1-9
Author(s):  
Huilin Zhou ◽  
Huimin Zheng ◽  
Qiegen Liu ◽  
Jian Liu ◽  
Yuhao Wang
Keyword(s):  
Inverse Scattering ◽  
Optimization Methods ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Scattering Problems ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Highly Nonlinear ◽  
Electromagnetic Inverse Scattering

Abstract Electromagnetic inverse-scattering problems (ISPs) are concerned with determining the properties of an unknown object using measured scattered fields. ISPs are often highly nonlinear, causing the problem to be very difficult to address. In addition, the reconstruction images of different optimization methods are distorted which leads to inaccurate reconstruction results. To alleviate these issues, we propose a new linear model solution of generative adversarial network-based (LM-GAN) inspired by generative adversarial networks (GAN). Two sub-networks are trained alternately in the adversarial framework. A linear deep iterative network as a generative network captures the spatial distribution of the data, and a discriminative network estimates the probability of a sample from the training data. Numerical results validate that LM-GAN has admirable fidelity and accuracy when reconstructing complex scatterers.

scholarly journals Generative Adversarial Networks-Based Semi-Supervised Automatic Modulation Recognition for Cognitive Radio Networks

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3913 ◽  
Author(s):  
Mingxuan Li ◽  
Ou Li ◽  
Guangyi Liu ◽  
Ce Zhang
Keyword(s):  
Deep Learning ◽  
Cognitive Radio ◽  
Supervised Learning ◽  
Rapid Development ◽  
Generative Adversarial Networks ◽  
Modulation Recognition ◽  
Learning Methods ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Automatic Modulation Recognition

With the recently explosive growth of deep learning, automatic modulation recognition has undergone rapid development. Most of the newly proposed methods are dependent on large numbers of labeled samples. We are committed to using fewer labeled samples to perform automatic modulation recognition in the cognitive radio domain. Here, a semi-supervised learning method based on adversarial training is proposed which is called signal classifier generative adversarial network. Most of the prior methods based on this technology involve computer vision applications. However, we improve the existing network structure of a generative adversarial network by adding the encoder network and a signal spatial transform module, allowing our framework to address radio signal processing tasks more efficiently. These two technical improvements effectively avoid nonconvergence and mode collapse problems caused by the complexity of the radio signals. The results of simulations show that compared with well-known deep learning methods, our method improves the classification accuracy on a synthetic radio frequency dataset by 0.1% to 12%. In addition, we verify the advantages of our method in a semi-supervised scenario and obtain a significant increase in accuracy compared with traditional semi-supervised learning methods.

Antinoise Learning and Coalitional Game GAN

2021 ◽  
Author(s):  
Hongyou Chen ◽  
Hongjie He ◽  
Fan Chen ◽  
Yiming Zhu
Keyword(s):  
Image Quality ◽  
Real Data ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Coalitional Game ◽  
Generative Adversarial Network ◽  
Adversarial Learning ◽  
Network Convergence ◽  
The Stability ◽  
Zero Sum

Adversarial learning stability has an important influence on the generated image quality and convergence process in generative adversarial networks (GANs). Training dataset (real data) noise and the balance of game players have an impact on adversarial learning stability. In the gradient backpropagation of the discriminator, the noise samples increase the gradient variance. It can increase the uncertainty in the network convergence progress and affect stability. In the two-player zero-sum game, the game ability of the generator and discriminator is unbalanced. Generally, the game ability of the generator is weaker than that of the discriminator, which affects the stability. To improve the stability, an antinoise learning and coalitional game generative adversarial network (ANL-CG GAN) is proposed, which achieves this goal through the following two strategies. (i) In the real data loss function of the discriminator, an effective antinoise learning method is designed, which can improve the gradient variance and network convergence uncertainty. (ii) In the zero-sum game, a generator coalitional game module is designed to enhance its game ability, which can improve the balance between the generator and discriminator via a coalitional game strategy. To verify the performance of this model, the generated results of the designed GAN and other GAN models in CELEBA and CIFAR10 are compared and analyzed. Experimental results show that the novel GAN can improve adversarial learning stability, generate image quality, and reduce the number of training epochs.

Super-Resolution Based on Generative Adversarial Network for HRTEM Images

2021 ◽  
pp. 2154027
Author(s):  
Fuqi Mao ◽  
Xiaohan Guan ◽  
Ruoyu Wang ◽  
Wen Yue
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Information Structure ◽  
Rapid Development ◽  
Super Resolution ◽  
Training Data ◽  
Learning Technology ◽  
Hrtem Image ◽  
Generative Adversarial Network ◽  
Dual Regression

As an important tool to study the microstructure and properties of materials, High Resolution Transmission Electron Microscope (HRTEM) images can obtain the lattice fringe image (reflecting the crystal plane spacing information), structure image and individual atom image (which reflects the configuration of atoms or atomic groups in crystal structure). Despite the rapid development of HTTEM devices, HRTEM images still have limited achievable resolution for human visual system. With the rapid development of deep learning technology in recent years, researchers are actively exploring the Super-resolution (SR) model based on deep learning, and the model has reached the current best level in various SR benchmarks. Using SR to reconstruct high-resolution HRTEM image is helpful to the material science research. However, there is one core issue that has not been resolved: most of these super-resolution methods require the training data to exist in pairs. In actual scenarios, especially for HRTEM images, there are no corresponding HR images. To reconstruct high quality HRTEM image, a novel Super-Resolution architecture for HRTEM images is proposed in this paper. Borrowing the idea from Dual Regression Networks (DRN), we introduce an additional dual regression structure to ESRGAN, by training the model with unpaired HRTEM images and paired nature images. Results of extensive benchmark experiments demonstrate that the proposed method achieves better performance than the most resent SISR methods with both quantitative and visual results.

Deep learning to identify and predict cardiotoxicities of anticancer drugs.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e15012-e15012
Author(s):  
Mayur Sarangdhar ◽  
Venkatesh Kolli ◽  
William Seibel ◽  
John Peter Perentesis
Keyword(s):  
Deep Learning ◽  
Alkylating Agents ◽  
Adverse Event Reporting System ◽  
Training Data ◽  
Training Dataset ◽  
Cancer Drugs ◽  
Conduction Abnormalities ◽  
Tree Classifier ◽  
Anti Cancer ◽  
Safety Signals

e15012 Background: Recent advances in cancer treatment have revolutionized patient outcomes. However, toxicities associated with anti-cancer drugs remain a concern with many anti-cancer drugs now implicated in cardiotoxicity. The complete spectrum of cardiotoxicity associated with anti-cancer drugs is only evident post-approval of drugs. Deep Learning methods can identify novel and emerging safety signals in “real-world” clinical settings. Methods: We used AERS Mine, an open-source data mining platform to identify drug toxicity signatures in the FDA’s Adverse Event Reporting System of 16 million patients. We identified 1.3 million patients on traditional and targeted anti-cancer therapy to analyze therapy-specific cardiotoxicity patterns. Cardiotoxicity training dataset contained 1571 molecules characterized with bioassay against hERG potassium channel and included 350 toxic compounds with an IC50 of < 1μM. We implemented a Deep Belief Network to extract a deep hierarchical representation of the training data, and the Extra Tree Classifier to predict the toxicity of drug candidates. Drugs were encoded using 1024-bit Morgan fingerprint representation using SMILES with search radius of 7 atoms. Pharmacovigilance metrics (Relative Risks and safety signals) were used to establish statistical correlation. Results: This analysis identified signatures of arrhythmias and conduction abnormalities associated with common anti-cancer drugs (e.g. atrial fibrillation with ibrutinib, alkylating agents, immunomodulatory drugs; sinus bradycardia with 5FU, paclitaxel, thalidomide; sinus tachycardia with anthracyclines). Our analysis also identified myositis/myocarditis association with newer immune checkpoint inhibitors (e.g., atezolizumab, durvalumab, cemiplimab, avelumab) paralleling earlier signals for pembrolizumab, nivolumab, and ipilimumab. Deep Learning identified signatures of chemical moieties linked to cardiotoxicity, including common motifs in drugs associated with arrhythmias and conduction abnormalities with an accuracy of 89%. Conclusions: Deep Learning provides a comprehensive insight into emerging cardiotoxicity patterns of approved and investigational drugs, allows detection of ‘rogue’ chemical moieties, and shows promise for novel drug discovery and development.

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