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

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.

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Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks

BMC Bioinformatics ◽

10.1186/s12859-021-04101-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Yingxi Yang ◽

Hui Wang ◽

Wen Li ◽

Xiaobo Wang ◽

Shizhao Wei ◽

...

Keyword(s):

Correlation Coefficient ◽

Sequence Data ◽

Rapid Development ◽

Pearson Correlation ◽

Structural Features ◽

Generative Adversarial Networks ◽

Post Translational Modification ◽

Generative Adversarial Network ◽

Data Imbalance ◽

Adversarial Network

Abstract Background Protein post-translational modification (PTM) is a key issue to investigate the mechanism of protein’s function. With the rapid development of proteomics technology, a large amount of protein sequence data has been generated, which highlights the importance of the in-depth study and analysis of PTMs in proteins. Method We proposed a new multi-classification machine learning pipeline MultiLyGAN to identity seven types of lysine modified sites. Using eight different sequential and five structural construction methods, 1497 valid features were remained after the filtering by Pearson correlation coefficient. To solve the data imbalance problem, Conditional Generative Adversarial Network (CGAN) and Conditional Wasserstein Generative Adversarial Network (CWGAN), two influential deep generative methods were leveraged and compared to generate new samples for the types with fewer samples. Finally, random forest algorithm was utilized to predict seven categories. Results In the tenfold cross-validation, accuracy (Acc) and Matthews correlation coefficient (MCC) were 0.8589 and 0.8376, respectively. In the independent test, Acc and MCC were 0.8549 and 0.8330, respectively. The results indicated that CWGAN better solved the existing data imbalance and stabilized the training error. Alternatively, an accumulated feature importance analysis reported that CKSAAP, PWM and structural features were the three most important feature-encoding schemes. MultiLyGAN can be found at https://github.com/Lab-Xu/MultiLyGAN. Conclusions The CWGAN greatly improved the predictive performance in all experiments. Features derived from CKSAAP, PWM and structure schemes are the most informative and had the greatest contribution to the prediction of PTM.

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

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-2-2020-573-2020 ◽

2020 ◽

Vol V-2-2020 ◽

pp. 573-582 ◽

Cited By ~ 1

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.

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A Hyperspectral Image Classification Method Based on Multi-Discriminator Generative Adversarial Networks

Sensors ◽

10.3390/s19153269 ◽

2019 ◽

Vol 19 (15) ◽

pp. 3269 ◽

Cited By ~ 8

Author(s):

Hongmin Gao ◽

Dan Yao ◽

Mingxia Wang ◽

Chenming Li ◽

Haiyun Liu ◽

...

Keyword(s):

Deep Learning ◽

Hyperspectral Image ◽

Hyperspectral Data ◽

Generative Adversarial Networks ◽

Data Sets ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

The One ◽

Hyperspectral Remote Sensing Images

Hyperspectral remote sensing images (HSIs) have great research and application value. At present, deep learning has become an important method for studying image processing. The Generative Adversarial Network (GAN) model is a typical network of deep learning developed in recent years and the GAN model can also be used to classify HSIs. However, there are still some problems in the classification of HSIs. On the one hand, due to the existence of different objects with the same spectrum phenomenon, if only according to the original GAN model to generate samples from spectral samples, it will produce the wrong detailed characteristic information. On the other hand, the gradient disappears in the original GAN model and the scoring ability of a single discriminator limits the quality of the generated samples. In order to solve the above problems, we introduce the scoring mechanism of multi-discriminator collaboration and complete semi-supervised classification on three hyperspectral data sets. Compared with the original GAN model with a single discriminator, the adjusted criterion is more rigorous and accurate and the generated samples can show more accurate characteristics. Aiming at the pattern collapse and diversity deficiency of the original GAN generated by single discriminator, this paper proposes a multi-discriminator generative adversarial networks (MDGANs) and studies the influence of the number of discriminators on the classification results. The experimental results show that the introduction of multi-discriminator improves the judgment ability of the model, ensures the effect of generating samples, solves the problem of noise in generating spectral samples and can improve the classification effect of HSIs. At the same time, the number of discriminators has different effects on different data sets.

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CBNWI-50: A Deep Learning Bird Dataset for Image Translation and Resolution Improvement using Generative Adversarial Network

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i1015.0789s19 ◽

2019 ◽

Vol 8 (9S) ◽

pp. 91-102

Keyword(s):

Deep Learning ◽

Super Resolution ◽

Generative Adversarial Networks ◽

Western India ◽

Generative Adversarial Network ◽

Style Transfer ◽

Adversarial Network ◽

Image Translation ◽

Common Birds ◽

Single Image Super Resolution

Generative Adversarial Networks have gained prominence in a short span of time as they can synthesize images from latent noise by minimizing the adversarial cost function. New variants of GANs have been developed to perform specific tasks using state-of-the-art GAN models, like image translation, single image super resolution, segmentation, classification, style transfer etc. However, a combination of two GANs to perform two different applications in one model has been sparsely explored. Hence, this paper concatenates two GANs and aims to perform Image Translation using Cycle GAN model on bird images and improve their resolution using SRGAN. During the extensive survey, it is observed that most of the deep learning databases on Aves were built using the new world species (i.e. species found in North America). Hence, to bridge this gap, a new Ave database, 'Common Birds of North - Western India' (CBNWI-50), is also proposed in this work.

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Application of an Improved DCGAN for Image GenerationApplication of an Improved DCGAN for Image Generation

10.21203/rs.3.rs-266104/v1 ◽

2021 ◽

Author(s):

Bingqi Liu ◽

Jiwei Lv ◽

Xinyue Fan ◽

Jie Luo ◽

Tianyi Zou

Keyword(s):

Rapid Development ◽

Activation Function ◽

Generative Adversarial Networks ◽

Generation Model ◽

Image Generation ◽

Generative Adversarial Network ◽

Research Areas ◽

Adversarial Network ◽

Fully Connected ◽

Initial Fraction

Abstract With the rapid development of deep learning, image generation technology has become one of the current hot research areas. A deep convolutional generative adversarial network (DCGAN) can better adapt to complex image distributions than other methods. In this paper, based on a traditional generative adversarial networks (GANs) image generation model, first, the fully connected layer of the DCGAN is further improved. To solve the problem of gradient disappearance in GANs, the activation functions of all layers of the discriminator are LeakyReLU functions, the output layer of the generator uses the Tanh activation function, and the other layers use ReLU. Second, the improved DCGAN model is verified on the MNIST dataset, and simple initial fraction (ISs) and complex initial fraction (ISc) indexes are established from the two aspects of image quality and image generation diversity, respectively. Finally, through a comparison of the two groups of experiments, it is found that the quality of images generated by the DCGAN model constructed in this paper is 2.02 higher than that of the GANs model, and the diversity of the images generated by the DCGAN is 1.55 higher than that of GANs. The results show that the improved DCGAN model can solve the problem of low-quality images being generated by the GANs and achieve good results.

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Semi-Supervised Learning for Seismic Impedance Inversion Using Generative Adversarial Networks

Remote Sensing ◽

10.3390/rs13050909 ◽

2021 ◽

Vol 13 (5) ◽

pp. 909

Author(s):

Bangyu Wu ◽

Delin Meng ◽

Haixia Zhao

Keyword(s):

Deep Learning ◽

Supervised Learning ◽

Unlabeled Data ◽

Forward Model ◽

Well Logs ◽

Generative Adversarial Networks ◽

Model Parameters ◽

Learning Methods ◽

Hydrocarbon Reservoir ◽

Impedance Inversion

Seismic impedance inversion is essential to characterize hydrocarbon reservoir and detect fluids in field of geophysics. However, it is nonlinear and ill-posed due to unknown seismic wavelet, observed data band limitation and noise, but it also requires a forward operator that characterizes physical relation between measured data and model parameters. Deep learning methods have been successfully applied to solve geophysical inversion problems recently. It can obtain results with higher resolution compared to traditional inversion methods, but its performance often not fully explored for the lack of adequate labeled data (i.e., well logs) in training process. To alleviate this problem, we propose a semi-supervised learning workflow based on generative adversarial network (GAN) for acoustic impedance inversion. The workflow contains three networks: a generator, a discriminator and a forward model. The training of the generator and discriminator are guided by well logs and constrained by unlabeled data via the forward model. The benchmark models Marmousi2, SEAM and a field data are used to demonstrate the performance of our method. Results show that impedance predicted by the presented method, due to making use of both labeled and unlabeled data, are better consistent with ground truth than that of conventional deep learning methods.

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Energy Efficiency Solutions for Buildings: Automated Fault Diagnosis of Air Handling Units Using Generative Adversarial Networks

Energies ◽

10.3390/en12030527 ◽

2019 ◽

Vol 12 (3) ◽

pp. 527 ◽

Cited By ~ 14

Author(s):

Chaowen Zhong ◽

Ke Yan ◽

Yuting Dai ◽

Ning Jin ◽

Bing Lou

Keyword(s):

Energy Efficiency ◽

Fault Diagnosis ◽

Supervised Learning ◽

Real World ◽

Generative Adversarial Networks ◽

Training Process ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Air Handling Units ◽

Real World Datasets

Automated fault diagnosis (AFD) for various energy consumption components is one of the main topics for energy efficiency solutions. However, the lack of faulty samples in the training process remains as a difficulty for data-driven AFD of heating, ventilation and air conditioning (HVAC) subsystems, such as air handling units (AHU). Existing works show that semi-supervised learning theories can effectively alleviate the issue by iteratively inserting newly tested faulty data samples into the training pool when the same fault happens again. However, a research gap exists between theoretical AFD algorithms and real-world applications. First, for real-world AFD applications, it is hard to predict the time when the same fault happens again. Second, the training set is required to be pre-defined and fixed before being packed into the building management system (BMS) for automatic HVAC fault diagnosis. The semi-supervised learning process of iteratively absorbing testing data into the training pool can be irrelevant for industrial usage of the AFD methods. Generative adversarial network (GAN) is well-known as an unsupervised learning technique to enrich the training pool with fake samples that are close to real faulty samples. In this study, a hybrid generative adversarial network (GAN) is proposed combining Wasserstein GAN with traditional classifiers to perform fault diagnosis mimicking the real-world scenarios with limited faulty training samples in the training process. Experimental results on real-world datasets demonstrate the effectiveness of the proposed approach for fault diagnosis problems of AHU subsystem.

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Generative adversarial networks for modeling reservoirs with permeability anisotropy

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1201/1/012066 ◽

2021 ◽

Vol 1201 (1) ◽

pp. 012066

Author(s):

R Guliev

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Oil Field ◽

Main Element ◽

Generative Adversarial Networks ◽

Geological Model ◽

Generative Adversarial Network ◽

Field Development ◽

Adversarial Network ◽

Permeability Data

Abstract The geological model is a main element in describing the characteristics of hydrocarbon reservoirs. These models are usually obtained using geostatistical modeling techniques. Recently, methods based on deep learning algorithms have begun to be applied as a generator of a geologic models. However, there are still problems with how to assimilate dynamic data to the model. The goal of this work was to develop a deep learning algorithm - generative adversarial network (GAN) and demonstrate the process of generating a synthetic geological model: • Without integrating permeability data into the model • With data assimilation of well permeability data into the model The authors also assessed the possibility of creating a pair of generative-adversarial network-ensemble smoother to improve the closed-loop reservoir management of oil field development.

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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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Deep TEC: Deep Transfer Learning with Ensemble Classifier for Road Extraction from UAV Imagery

Remote Sensing ◽

10.3390/rs12020245 ◽

2020 ◽

Vol 12 (2) ◽

pp. 245 ◽

Cited By ~ 6

Author(s):

J. Senthilnath ◽

Neelanshi Varia ◽

Akanksha Dokania ◽

Gaotham Anand ◽

Jón Atli Benediktsson

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Transfer Learning ◽

Ensemble Classifier ◽

Urban Region ◽

Road Extraction ◽

Learning Methods ◽

Generative Adversarial Network ◽

The Road ◽

Adversarial Network

Unmanned aerial vehicle (UAV) remote sensing has a wide area of applications and in this paper, we attempt to address one such problem—road extraction from UAV-captured RGB images. The key challenge here is to solve the road extraction problem using the UAV multiple remote sensing scene datasets that are acquired with different sensors over different locations. We aim to extract the knowledge from a dataset that is available in the literature and apply this extracted knowledge on our dataset. The paper focuses on a novel method which consists of deep TEC (deep transfer learning with ensemble classifier) for road extraction using UAV imagery. The proposed deep TEC performs road extraction on UAV imagery in two stages, namely, deep transfer learning and ensemble classifier. In the first stage, with the help of deep learning methods, namely, the conditional generative adversarial network, the cycle generative adversarial network and the fully convolutional network, the model is pre-trained on the benchmark UAV road extraction dataset that is available in the literature. With this extracted knowledge (based on the pre-trained model) the road regions are then extracted on our UAV acquired images. Finally, for the road classified images, ensemble classification is carried out. In particular, the deep TEC method has an average quality of 71%, which is 10% higher than the next best standard deep learning methods. Deep TEC also shows a higher level of performance measures such as completeness, correctness and F1 score measures. Therefore, the obtained results show that the deep TEC is efficient in extracting road networks in an urban region.

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