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

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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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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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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EVALUATION OF SAR TO OPTICAL IMAGE TRANSLATION USING CONDITIONAL GENERATIVE ADVERSARIAL NETWORK FOR CLOUD REMOVAL IN A CROP DATASET

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

10.5194/isprs-archives-xliii-b3-2021-823-2021 ◽

2021 ◽

Vol XLIII-B3-2021 ◽

pp. 823-828

Author(s):

L. E. Christovam ◽

M. H. Shimabukuro ◽

M. L. B. T. Galo ◽

E. Honkavaara

Keyword(s):

Time Series ◽

Visual Analysis ◽

Optical Image ◽

Optical Data ◽

Image Pair ◽

Generative Adversarial Network ◽

The Real ◽

Adversarial Network ◽

Sensing Applications ◽

Cloud Removal

Abstract. Most methods developed to map crop fields with high-quality are based on optical image time-series. However, often accuracy of these approaches is deteriorated due to clouds and cloud shadows, which can decrease the availably of optical data required to represent crop phenological stages. In this sense, the objective of this study was to implement and evaluate the conditional Generative Adversarial Network (cGAN) that has been indicated as a potential tool to address the cloud and cloud shadow removal; we also compared it with the Witthaker Smother (WS), which is a well-known data cleaning algorithm. The dataset used to train and assess the methods was the Luis Eduardo Magalhães benchmark for tropical agricultural remote sensing applications. We selected one MSI/Sentinel-2 and C-SAR/Sentinel-1 image pair taken in days as close as possible. A total of 5000 image pair patches were generated to train the cGAN model, which was used to derive synthetic optical pixels for a testing area. Visual analysis, spectral behaviour comparison, and classification were used to evaluate and compare the pixels generated with the cGAN and WS against the pixel values from the real image. The cGAN provided consistent pixel values for most crop types in comparison to the real pixel values and outperformed the WS significantly. The results indicated that the cGAN has potential to fill cloud and cloud shadow gaps in optical image time-series.

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Conditional Generative Adversarial Network-Based Training Sample Set Improvement Model for the Semantic Segmentation of High-Resolution Remote Sensing Images

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2020.3033816 ◽

2020 ◽

pp. 1-17

Author(s):

Xin Pan ◽

Jian Zhao ◽

Jun Xu

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Semantic Segmentation ◽

Training Sample ◽

Remote Sensing Images ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Sample Set

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Improving Road Semantic Segmentation Using Generative Adversarial Network

IEEE Access ◽

10.1109/access.2021.3075951 ◽

2021 ◽

Vol 9 ◽

pp. 64381-64392

Author(s):

Abolfazl Abdollahi ◽

Biswajeet Pradhan ◽

Gaurav Sharma ◽

Khairul Nizam Abdul Maulud ◽

Abdullah Alamri

Keyword(s):

Semantic Segmentation ◽

Generative Adversarial Network ◽

Adversarial Network

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Network anomaly detection with net-GAN, a generative adversarial network for analysis of multivariate time-series

10.1145/3405837.3411393 ◽

2020 ◽

Author(s):

Gastón García González ◽

Pedro Casas ◽

Alicia Fenández ◽

Gabriel Gómez

Keyword(s):

Time Series ◽

Anomaly Detection ◽

Multivariate Time Series ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Network Anomaly Detection

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Cognitive Covert Traffic Synthesis Method Based on Generative Adversarial Network

Wireless Communications and Mobile Computing ◽

10.1155/2021/9982351 ◽

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.

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WHITE-BOX CARTOONIZATION USING AN EXTENDED GAN FRAMEWORK

International Journal of Engineering Applied Sciences and Technology ◽

10.33564/ijeast.2021.v05i12.049 ◽

2021 ◽

Vol 5 (12) ◽

Author(s):

Amey Thakur ◽

Hasan Rizvi ◽

Mega Satish

Keyword(s):

Smooth Surface ◽

Generative Models ◽

Surface Representation ◽

High Quality ◽

Generative Adversarial Network ◽

Structure Representation ◽

Texture Representation ◽

Adversarial Network ◽

Representation Structure ◽

New Framework

In the present study, we propose to implement a new framework for estimating generative models via an adversarial process to extend an existing GAN framework and develop a white-box controllable image cartoonization, which can generate high-quality cartooned images/videos from real-world photos and videos. The learning purposes of our system are based on three distinct representations: surface representation, structure representation, and texture representation. The surface representation refers to the smooth surface of the images. The structure representation relates to the sparse colour blocks and compresses generic content. The texture representation shows the texture, curves, and features in cartoon images. Generative Adversarial Network (GAN) framework decomposes the images into different representations and learns from them to generate cartoon images. This decomposition makes the framework more controllable and flexible which allows users to make changes based on the required output. This approach overcomes any previous system in terms of maintaining clarity, colours, textures, shapes of images yet showing the characteristics of cartoon images.

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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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