Effect of Annotation and Loss Function on Epiphyte Identification using Conditional Generative Adversarial Network

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.

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

10.21203/rs.3.rs-95571/v1 ◽

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.

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A NEPHOGRAM PREDICTION METHOD BASED ON GENERATIVE ADVERSARIAL NETWORK

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w10-925-2020 ◽

2020 ◽

Vol XLII-3/W10 ◽

pp. 925-930

Author(s):

Y. Xun ◽

W. Q. Yu

Keyword(s):

Loss Function ◽

Internal Representation ◽

Prediction Method ◽

Ground Truth ◽

Generative Adversarial Network ◽

Multi Scale ◽

Adversarial Network ◽

Basic Characteristics ◽

Cloud Evolution

Abstract. As one of the important sources of meteorological information, satellite nephogram is playing an increasingly important role in the detection and forecast of disastrous weather. The predictions about the movement and transformation of cloud with certain timeliness can enhance the practicability of satellite nephogram. Based on the generative adversarial network in unsupervised learning, we propose a prediction model of time series nephogram, which construct the internal representation of cloud evolution accurately and realize nephogram prediction for the next several hours. We improve the traditional generative adversarial network by constructing the generator and discriminator used the multi-scale convolution network. After the scale transform process, different scales operate convolutions in parallel and then merge the features. This structure can solve the problem of long-term dependence in the traditional network, and both global and detailed features are considered. Then according to the network structure and practical application, we define a new loss function combined with adversarial loss function to accelerate the convergence of model and sharpen predictions which keeps the effectivity of predictions further. Our method has no need to carry out the stack mathematics calculation and the manual operations, has greatly enhanced the feasibility and the efficiency. The results show that this model can reasonably describe the basic characteristics and evolution trend of cloud cluster, the prediction nephogram has very high similarity to the ground-truth nephogram.

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PaDGAN: Learning to Generate High-Quality Novel Designs

Journal of Mechanical Design ◽

10.1115/1.4048626 ◽

2020 ◽

Vol 143 (3) ◽

Author(s):

Wei Chen ◽

Faez Ahmed

Keyword(s):

Loss Function ◽

Design Space Exploration ◽

Design Space ◽

Space Exploration ◽

Generative Models ◽

Training Data ◽

High Quality ◽

Generative Adversarial Network ◽

Design Synthesis ◽

Adversarial Network

Abstract Deep generative models are proven to be a useful tool for automatic design synthesis and design space exploration. When applied in engineering design, existing generative models face three challenges: (1) generated designs lack diversity and do not cover all areas of the design space, (2) it is difficult to explicitly improve the overall performance or quality of generated designs, and (3) existing models generally do not generate novel designs, outside the domain of the training data. In this article, we simultaneously address these challenges by proposing a new determinantal point process-based loss function for probabilistic modeling of diversity and quality. With this new loss function, we develop a variant of the generative adversarial network, named “performance augmented diverse generative adversarial network” (PaDGAN), which can generate novel high-quality designs with good coverage of the design space. By using three synthetic examples and one real-world airfoil design example, we demonstrate that PaDGAN can generate diverse and high-quality designs. In comparison to a vanilla generative adversarial network, on average, it generates samples with a 28% higher mean quality score with larger diversity and without the mode collapse issue. Unlike typical generative models that usually generate new designs by interpolating within the boundary of training data, we show that PaDGAN expands the design space boundary outside the training data towards high-quality regions. The proposed method is broadly applicable to many tasks including design space exploration, design optimization, and creative solution recommendation.

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Wasserstein Generative Adversarial Network Based De-Blurring Using Perceptual Similarity

Applied Sciences ◽

10.3390/app9112358 ◽

2019 ◽

Vol 9 (11) ◽

pp. 2358 ◽

Cited By ~ 1

Author(s):

Minsoo Hong ◽

Yoonsik Choe

Keyword(s):

Neural Network ◽

Loss Function ◽

Structural Similarity ◽

Wasserstein Distance ◽

Perceptual Similarity ◽

Generative Adversarial Network ◽

Edge Information ◽

Adversarial Network ◽

Blurred Images ◽

Parameter Values

The de-blurring of blurred images is one of the most important image processing methods and it can be used for the preprocessing step in many multimedia and computer vision applications. Recently, de-blurring methods have been performed by neural network methods, such as the generative adversarial network (GAN), which is a powerful generative network. Among many different types of GAN, the proposed method is performed using the Wasserstein generative adversarial network with gradient penalty (WGANGP). Since edge information is the most important factor in an image, the style loss function is applied to represent the perceptual information of the edge in order to preserve small edge information and capture its perceptual similarity. As a result, the proposed method improves the similarity between sharp and blurred images by minimizing the Wasserstein distance, and it captures well the perceptual similarity using the style loss function, considering the correlation of features in the convolutional neural network (CNN). To confirm the performance of the proposed method, three experiments are conducted using two datasets: the GOPRO Large and Kohler dataset. The optimal solutions are found by changing the parameter values experimentally. Consequently, the experiments depict that the proposed method achieves 0.98 higher performance in structural similarity (SSIM) and outperforms other de-blurring methods in the case of both datasets.

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Oversampling Imbalanced Data Based on Convergent WGAN for Network Threat Detection

Security and Communication Networks ◽

10.1155/2021/9206440 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Yanping Xu ◽

Xiaoyu Zhang ◽

Zhenliang Qiu ◽

Xia Zhang ◽

Jian Qiu ◽

...

Keyword(s):

Nash Equilibrium ◽

Loss Function ◽

Class Imbalance ◽

Imbalanced Data ◽

Real Data ◽

Threat Detection ◽

Training Process ◽

Generative Adversarial Network ◽

Minority Class ◽

Adversarial Network

Class imbalance is a common problem in network threat detection. Oversampling the minority class is regarded as a popular countermeasure by generating enough new minority samples. Generative adversarial network (GAN) is a typical generative model that can generate any number of artificial minority samples, which are close to the real data. However, it is difficult to train GAN, and the Nash equilibrium is almost impossible to achieve. Therefore, in order to improve the training stability of GAN for oversampling to detect the network threat, a convergent WGAN-based oversampling model called convergent WGAN (CWGAN) is proposed in this paper. The training process of CWGAN contains multiple iterations. In each iteration, the training epochs of the discriminator are dynamic, which is determined by the convergence of discriminator loss function in the last two iterations. When the discriminator is trained to convergence, the generator will then be trained to generate new minority samples. The experiment results show that CWGAN not only improve the training stability of WGAN on the loss smoother and closer to 0 but also improve the performance of the minority class through oversampling, which means that CWGAN can improve the performance of network threat detection.

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PaDGAN: A Generative Adversarial Network for Performance Augmented Diverse Designs

Volume 11A: 46th Design Automation Conference (DAC) ◽

10.1115/detc2020-22729 ◽

2020 ◽

Author(s):

Wei Chen ◽

Faez Ahmed

Keyword(s):

Loss Function ◽

Design Space Exploration ◽

Point Processes ◽

Design Space ◽

Space Exploration ◽

Generative Models ◽

Training Data ◽

High Quality ◽

Generative Adversarial Network ◽

Adversarial Network

Abstract Deep generative models are proven to be a useful tool for automatic design synthesis and design space exploration. When applied in engineering design, existing generative models face three challenges: 1) generated designs lack diversity and do not cover all areas of the design space, 2) it is difficult to explicitly improve the overall performance or quality of generated designs, and 3) existing models generate do not generate novel designs, outside the domain of the training data. In this paper, we simultaneously address these challenges by proposing a new Determinantal Point Processes based loss function for probabilistic modeling of diversity and quality. With this new loss function, we develop a variant of the Generative Adversarial Network, named “Performance Augmented Diverse Generative Adversarial Network” or PaDGAN, which can generate novel high-quality designs with good coverage of the design space. Using three synthetic examples and one real-world airfoil design example, we demonstrate that PaDGAN can generate diverse and high-quality designs. In comparison to a vanilla Generative Adversarial Network, on average, it generates samples with 28% higher mean quality score with larger diversity and without the mode collapse issue. Unlike typical generative models that usually generate new designs by interpolating within the boundary of training data, we show that PaDGAN expands the design space boundary outside the training data towards high-quality regions. The proposed method is broadly applicable to many tasks including design space exploration, design optimization, and creative solution recommendation.

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Pix2pix Conditional Generative Adversarial Network with MLP Loss Function for Cloud Removal in a Cropland Time Series

Remote Sensing ◽

10.3390/rs14010144 ◽

2021 ◽

Vol 14 (1) ◽

pp. 144

Author(s):

Luiz E. Christovam ◽

Milton H. Shimabukuro ◽

Maria de Lourdes B. T. Galo ◽

Eija Honkavaara

Keyword(s):

Time Series ◽

Loss Function ◽

Visual Analysis ◽

Spectral Response ◽

Semantic Segmentation ◽

Generative Models ◽

Synthetic Image ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Crop Type

Clouds are one of the major limitations to crop monitoring using optical satellite images. Despite all efforts to provide decision-makers with high-quality agricultural statistics, there is still a lack of techniques to optimally process satellite image time series in the presence of clouds. In this regard, in this article it was proposed to add a Multi-Layer Perceptron loss function to the pix2pix conditional Generative Adversarial Network (cGAN) objective function. The aim was to enforce the generative model to learn how to deliver synthetic pixels whose values were proxies for the spectral response improving further crop type mapping. Furthermore, it was evaluated the generalization capacity of the generative models in producing pixels with plausible values for images not used in the training. To assess the performance of the proposed approach it was compared real images with synthetic images generated with the proposed approach as well as with the original pix2pix cGAN. The comparative analysis was performed through visual analysis, pixel values analysis, semantic segmentation and similarity metrics. In general, the proposed approach provided slightly better synthetic pixels than the original pix2pix cGAN, removing more noise than the original pix2pix algorithm as well as providing better crop type semantic segmentation; the semantic segmentation of the synthetic image generated with the proposed approach achieved an F1-score of 44.2%, while the real image achieved 44.7%. Regarding the generalization, the models trained utilizing different regions of the same image provided better pixels than models trained using other images in the time series. Besides this, the experiments also showed that the models trained using a pair of images selected every three months along the time series also provided acceptable results on images that do not have cloud-free areas.

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Interactive Removal of Microphone Object in Facial Images

Electronics ◽

10.3390/electronics8101115 ◽

2019 ◽

Vol 8 (10) ◽

pp. 1115 ◽

Cited By ~ 4

Author(s):

Muhammad Kamran Javed Khan ◽

Nizam Ud Din ◽

Seho Bae ◽

Juneho Yi

Keyword(s):

Loss Function ◽

Ground Truth ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Object Removal ◽

Extensive Evaluation ◽

Specific Object ◽

Image Pairs ◽

Two Stages ◽

Facial Images

Removing a specific object from an image and replacing the hole left behind with visually plausible backgrounds is a very intriguing task. While recent deep learning based object removal methods have shown promising results on this task for some structured scenes, none of them have addressed the problem of object removal in facial images. The objective of this work is to remove microphone object in facial images and fill hole with correct facial semantics and fine details. To make our solution practically useful, we present an interactive method called MRGAN, where the user roughly provides the microphone region. For filling the hole, we employ a Generative Adversarial Network based image-to-image translation approach. We break the problem into two stages: inpainter and refiner. The inpainter estimates coarse prediction by roughly filling in the microphone region followed by the refiner which produces fine details under the microphone region. We unite perceptual loss, reconstruction loss and adversarial loss as joint loss function for generating a realistic face and similar structure to the ground truth. Because facial image pairs with or without microphone do not exist, we have trained our method on a synthetically generated microphone dataset from CelebA face images and evaluated on real world microphone images. Our extensive evaluation shows that MRGAN performs better than state-of-the-art image manipulation methods on real microphone images although we only train our method using the synthetic dataset created. Additionally, we provide ablation studies for the integrated loss function and for different network arrangements.

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A Generative Adversarial Network with Dual Discriminators for Infrared and Visible Image Fusion Based on Saliency Detection

Mathematical Problems in Engineering ◽

10.1155/2021/4209963 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Dazhi Zhang ◽

Jilei Hou ◽

Wei Wu ◽

Tao Lu ◽

Huabing Zhou

Keyword(s):

Image Fusion ◽

Loss Function ◽

Network Architecture ◽

Saliency Detection ◽

Infrared Image ◽

Saliency Map ◽

Generative Adversarial Network ◽

Visible Image ◽

Adversarial Network ◽

Salient Regions

Infrared and visible image fusion needs to preserve both the salient target of the infrared image and the texture details of the visible image. Therefore, an infrared and visible image fusion method based on saliency detection is proposed. Firstly, the saliency map of the infrared image is obtained by saliency detection. Then, the specific loss function and network architecture are designed based on the saliency map to improve the performance of the fusion algorithm. Specifically, the saliency map is normalized to [0, 1], used as a weight map to constrain the loss function. At the same time, the saliency map is binarized to extract salient regions and nonsalient regions. And, a generative adversarial network with dual discriminators is obtained. The two discriminators are used to distinguish the salient regions and the nonsalient regions, respectively, to promote the generator to generate better fusion results. The experimental results show that the fusion results of our method are better than those of the existing methods in both subjective and objective aspects.

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