scholarly journals ANALYSIS OF FOUR GENERATOR ARCHITECTURES OF C-GAN, LOSS FUNCTION, AND ANNOTATION METHOD FOR EPIPHYTE IDENTIFICATION

2021 ◽  
Vol XLIV-M-3-2021 ◽  
pp. 149-153
Author(s):  
V. V. Sajithvariyar ◽  
S. Aswin ◽  
V. Sowmya ◽  
K. P. Soman ◽  
R. Sivanpillai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Parameter Tuning ◽  
Cross Entropy ◽  
Baseline Model ◽  
Generative Adversarial Network ◽  
Annotation Method ◽  
Adversarial Network ◽  
Reliability And Robustness ◽  
Deep Learning Model

Abstract. The deep learning (DL) models require timely updates to continue their reliability and robustness in prediction, classification, and segmentation tasks. When the deep learning models are tested with a limited test set, the model will not reveal the drawbacks. Every deep learning baseline model needs timely updates by incorporating more data, change in architecture, and hyper parameter tuning. This work focuses on updating the Conditional Generative Adversarial Network (C-GAN) based epiphyte identification deep learning model by incorporating 4 different generator architectures of GAN and two different loss functions. The four generator architectures used in this task are Resnet-6. Resnet-9, Resnet-50 and Resnet-101. A new annotation method called background removed annotation was tested to analyse the improvement in the epiphyte identification protocol. All the results obtained from the model by changing the above parameters are reported using two common evaluation metrics. Based on the parameter tuning experiment, Resnet-6, and Resnet- 9, with binary cross-entropy (BCE) as the loss function, attained higher scores also Resnet-6 with MSE as loss function performed well. The new annotation by removing the background had minimal effect on identifying the epiphytes.

Applying LSTM and GAN to build a deep learning model (TGAN-TEC) for global ionospheric TEC

2020 ◽  
Author(s):  
zhou chen ◽  
Yue deng ◽  
Jing-Song wang
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Training Data ◽  
Spatial And Temporal Variation ◽  
Electron Content ◽  
Generative Adversarial Network ◽  
Total Electron ◽  
Adversarial Network ◽  
Short Time ◽  
Deep Learning Model

<p>TEC is very important ionospheric parameter, which is commonly used observation for studying various ionospheric physical mechanism and other technological related to ionosphere (i.e. Global Positioning). However, the variation of global TEC is very dynamic, and its spatiotemporal variation is extremely complicated. So in this paper, we try to build a novel global ionospheric TEC (total electron content) predicting model based on two deep learning algorithms: generative adversarial network (GAN) and long short-term memory (LSTM). Training data is from 10-year IGS TEC data, which provide plenty of data for the GAN and LSTM algorithm to obtain the spatial and temporal variation of TEC respectively. The prediction accuracy of this model have been calculated under different levels of geomagnetic activity. The statistic result suggest that the proposed ionospheric model can be used as an efficient tool for ionospheric TEC short-time prediction.</p>

scholarly journals An Imbalanced Image Classification Method for the Cell Cycle Phase

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 249
Author(s):  
Xin Jin ◽  
Yuanwen Zou ◽  
Zhongbing Huang
Keyword(s):  
Cell Cycle ◽  
Deep Learning ◽  
Image Classification ◽  
Classification Accuracy ◽  
Data Augmentation ◽  
Cycle Phase ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Cellular Life

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

AN AI-BASED APPROACH TO ENHANCED FRACTURE RESOLUTION IN IMAGE LOGS

2021 ◽  
Author(s):  
James Howard ◽  
◽  
Joe Tracey ◽  
Mike Shen ◽  
Shawn Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Nearest Neighbor ◽  
Rock Fracture ◽  
Short Interval ◽  
Acoustic Properties ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Deep Learning Neural Network ◽  
Borehole Image

Borehole image logs are used to identify the presence and orientation of fractures, both natural and induced, found in reservoir intervals. The contrast in electrical or acoustic properties of the rock matrix and fluid-filled fractures is sufficiently large enough that sub-resolution features can be detected by these image logging tools. The resolution of these image logs is based on the design and operation of the tools, and generally is in the millimeter per pixel range. Hence the quantitative measurement of actual width remains problematic. An artificial intelligence (AI) -based workflow combines the statistical information obtained from a Machine-Learning (ML) segmentation process with a multiple-layer neural network that defines a Deep Learning process that enhances fractures in a borehole image. These new images allow for a more robust analysis of fracture widths, especially those that are sub-resolution. The images from a BHTV log were first segmented into rock and fluid-filled fractures using a ML-segmentation tool that applied multiple image processing filters that captured information to describe patterns in fracture-rock distribution based on nearest-neighbor behavior. The robust ML analysis was trained by users to identify these two components over a short interval in the well, and then the regression model-based coefficients applied to the remaining log. Based on the training, each pixel was assigned a probability value between 1.0 (being a fracture) and 0.0 (pure rock), with most of the pixels assigned one of these two values. Intermediate probabilities represented pixels on the edge of rock-fracture interface or the presence of one or more sub-resolution fractures within the rock. The probability matrix produced a map or image of the distribution of probabilities that determined whether a given pixel in the image was a fracture or partially filled with a fracture. The Deep Learning neural network was based on a Conditional Generative Adversarial Network (cGAN) approach where the probability map was first encoded and combined with a noise vector that acted as a seed for diverse feature generation. This combination was used to generate new images that represented the BHTV response. The second layer of the neural network, the adversarial or discriminator portion, determined whether the generated images were representative of the actual BHTV by comparing the generated images with actual images from the log and producing an output probability of whether it was real or fake. This probability was then used to train the generator and discriminator models that were then applied to the entire log. Several scenarios were run with different probability maps. The enhanced BHTV images brought out fractures observed in the core photos that were less obvious in the original BTHV log through enhanced continuity and improved resolution on fracture widths.

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.

scholarly journals Cognitive Covert Traffic Synthesis Method Based on Generative Adversarial Network

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhangguo Tang ◽  
Junfeng Wang ◽  
Huanzhou Li ◽  
Jian Zhang ◽  
Junhao Wang
Keyword(s):  
Loss Function ◽  
Learning Algorithm ◽  
Synthesis Method ◽  
Covert Channel ◽  
Covert Communication ◽  
New Paradigm ◽  
Generative Adversarial Network ◽  
Good Convergence ◽  
Adversarial Network ◽  
Activity Loss

In the intelligent era of human-computer symbiosis, the use of machine learning method for covert communication confrontation has become a hot topic of network security. The existing covert communication technology focuses on the statistical abnormality of traffic behavior and does not consider the sensory abnormality of security censors, so it faces the core problem of lack of cognitive ability. In order to further improve the concealment of communication, a game method of “cognitive deception” is proposed, which is aimed at eliminating the anomaly of traffic in both behavioral and cognitive dimensions. Accordingly, a Wasserstein Generative Adversarial Network of Covert Channel (WCCGAN) model is established. The model uses the constraint sampling of cognitive priors to construct the constraint mechanism of “functional equivalence” and “cognitive equivalence” and is trained by a dynamic strategy updating learning algorithm. Among them, the generative module adopts joint expression learning which integrates network protocol knowledge to improve the expressiveness and discriminability of traffic cognitive features. The equivalent module guides the discriminant module to learn the pragmatic relevance features through the activity loss function of traffic and the application loss function of protocol for end-to-end training. The experimental results show that WCCGAN can directly synthesize traffic with comprehensive concealment ability, and its behavior concealment and cognitive deception are as high as 86.2% and 96.7%, respectively. Moreover, the model has good convergence and generalization ability and does not depend on specific assumptions and specific covert algorithms, which realizes a new paradigm of cognitive game in covert communication.

scholarly journals Best Selection of Generative Adversarial Networks Hyper-Parameters using Genetic Algorithm

2020 ◽  
Author(s):  
Fajr Alarsan ◽  
Mamoon Younes
Keyword(s):  
Genetic Algorithm ◽  
Programming Language ◽  
Loss Function ◽  
Generative Adversarial Networks ◽  
Accuracy Function ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Parameters Selection ◽  
Selection Of

Abstract Generative Adversarial Networks (GANs) are most popular generative frameworks that have achieved compelling performance. They follow an adversarial approach where two deep models generator and discriminator compete with each other In this paper, we propose a Generative Adversarial Network with best hyper-parameters selection to generate fake images for digits number 1 to 9 with generator and train discriminator to decide whereas the generated images are fake or true. Using Genetic Algorithm technique to adapt GAN hyper-parameters, the final method is named GANGA:Generative Adversarial Network with Genetic Algorithm. Anaconda environment with tensorflow library facilitates was used, python as programming language also used with needed libraries. The implementation was done using MNIST dataset to validate our work. The proposed method is to let Genetic algorithm to choose best values of hyper-parameters depending on minimizing a cost function such as a loss function or maximizing accuracy function. GA was used to select values of Learning rate, Batch normalization, Number of neurons and a parameter of Dropout layer.

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.

scholarly journals Improving CBCT Quality to CT Level using Deep‐Learning with Generative Adversarial Network

2020 ◽  
Author(s):  
Yang Zhang ◽  
Ning Yue ◽  
Min‐Ying Su ◽  
Bo Liu ◽  
Yi Ding ◽  
...  
Keyword(s):  
Deep Learning ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals A generative adversarial network for artifact removal in photoacoustic computed tomography with a linear-array transducer

2020 ◽  
Vol 245 (7) ◽  
pp. 597-605 ◽  
Author(s):  
Tri Vu ◽  
Mucong Li ◽  
Hannah Humayun ◽  
Yuan Zhou ◽  
Junjie Yao
Keyword(s):  
Deep Learning ◽  
Linear Array ◽  
Artifact Removal ◽  
Generative Adversarial Network ◽  
Transducer Array ◽  
Limited Bandwidth ◽  
Adversarial Network ◽  
Array Transducer ◽  
Imaging Speed

With balanced spatial resolution, penetration depth, and imaging speed, photoacoustic computed tomography (PACT) is promising for clinical translation such as in breast cancer screening, functional brain imaging, and surgical guidance. Typically using a linear ultrasound (US) transducer array, PACT has great flexibility for hand-held applications. However, the linear US transducer array has a limited detection angle range and frequency bandwidth, resulting in limited-view and limited-bandwidth artifacts in the reconstructed PACT images. These artifacts significantly reduce the imaging quality. To address these issues, existing solutions often have to pay the price of system complexity, cost, and/or imaging speed. Here, we propose a deep-learning-based method that explores the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) to reduce the limited-view and limited-bandwidth artifacts in PACT. Compared with existing reconstruction and convolutional neural network approach, our model has shown improvement in imaging quality and resolution. Our results on simulation, phantom, and in vivo data have collectively demonstrated the feasibility of applying WGAN-GP to improve PACT’s image quality without any modification to the current imaging set-up. Impact statement This study has the following main impacts. It offers a promising solution for removing limited-view and limited-bandwidth artifact in PACT using a linear-array transducer and conventional image reconstruction, which have long hindered its clinical translation. Our solution shows unprecedented artifact removal ability for in vivo image, which may enable important applications such as imaging tumor angiogenesis and hypoxia. The study reports, for the first time, the use of an advanced deep-learning model based on stabilized generative adversarial network. Our results have demonstrated its superiority over other state-of-the-art deep-learning methods.

scholarly journals Eigenloss: Combined PCA-Based Loss Function for Polyp Segmentation

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1316
Author(s):  
Luisa F. Sánchez-Peralta ◽  
Artzai Picón ◽  
Juan Antonio Antequera-Barroso ◽  
Juan Francisco Ortega-Morán ◽  
Francisco M. Sánchez-Margallo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Survival Rates ◽  
Principal Component ◽  
Loss Functions ◽  
Cancer Death ◽  
Linear Combinations ◽  
The Individual ◽  
Deep Learning Model ◽  
Death Causes

Colorectal cancer is one of the leading cancer death causes worldwide, but its early diagnosis highly improves the survival rates. The success of deep learning has also benefited this clinical field. When training a deep learning model, it is optimized based on the selected loss function. In this work, we consider two networks (U-Net and LinkNet) and two backbones (VGG-16 and Densnet121). We analyzed the influence of seven loss functions and used a principal component analysis (PCA) to determine whether the PCA-based decomposition allows for the defining of the coefficients of a non-redundant primal loss function that can outperform the individual loss functions and different linear combinations. The eigenloss is defined as a linear combination of the individual losses using the elements of the eigenvector as coefficients. Empirical results show that the proposed eigenloss improves the general performance of individual loss functions and outperforms other linear combinations when Linknet is used, showing potential for its application in polyp segmentation problems.

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