Human Face Sketch to RGB Image with Edge Optimization and Generative Adversarial Networks

Zhang, Feng; Zhao, Haitao; Wang, Ying; Liu, Qingyun; Raj, Alex Noel Joseph; Fu, Bin

doi:10.32604/iasc.2020.011750

Cited by 20 publications

(13 citation statements)

References 23 publications

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“…For example, in the network model, only one adversarial loss is generated in the direction propagation of a single discriminator, and one adversarial loss trains the two target layers of local inconsistency and semantic inconsistency separately. And, the confrontational loss caused by a single discriminator will make the training imbalance, resulting in poor convergence of the model [ 22 ]. Therefore, the adversarial network used in this study is the macro-micro adversarial network (MMAN).…”

Section: Clothing Analysis Based On Deep Learningmentioning

confidence: 99%

[Retracted] Study on 3D Clothing Color Application Based on Deep Learning‐Enabled Macro‐Micro Adversarial Network and Human Body Modeling

Liu

Ren

Qin³

2021

Computational Intelligence and Neuroscience

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In real life, people’s life gradually tends to be simple, so the convenience of online shopping makes more and more research begin to explore the convenience optimization of shopping, in which the fitting system is the research product. However, due to the immaturity of the virtual fitting system, there are a lot of problems, such as the expression of clothing color is not clear or deviation. In view of this, this paper proposes a 3D clothing color display model based on deep learning to support human modeling-driven. Firstly, the macro-micro adversarial network (MMAN) based on deep learning is used to analyze the original image, and then, the results are preprocessed. Finally, the 3D model with the original image color is constructed by using UV mapping. The experimental results show that the accuracy of the MMAN algorithm reaches 0.972, the established three-dimensional model is emotional enough, the expression of the clothing color is clear, and the difference between the color difference and the original image is within 0.01, and the subjective evaluation of volunteers is more than 90 points. The above results show that it is effective to use deep learning to build a 3D model with the original picture clothing color, which has great guiding significance for the research of character model modeling and simulation.

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Section: Clothing Analysis Based On Deep Learningmentioning

confidence: 99%

[Retracted] Study on 3D Clothing Color Application Based on Deep Learning‐Enabled Macro‐Micro Adversarial Network and Human Body Modeling

Liu

Ren

Qin³

2021

Computational Intelligence and Neuroscience

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“…Processing. In recent years, Generative Adversarial Network (GAN) has shown strong ability in many application fields [27][28][29][30] and has also made a lot of achievements in the field of traffic transformation and traffic synthesis. GAN is a kind of generative model [31], which learns data distribution and synthesizes sample data from distribution.…”

Section: Gan and Its Application In Trafficmentioning

confidence: 99%

Cognitive Covert Traffic Synthesis Method Based on Generative Adversarial Network

Tang

Wang

et al. 2021

Wireless Communications and Mobile Computing

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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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“…To use the advantages of CNNs in an unsupervised manner, this paper proposes to use them in a new two-stage GAN-based technique for structural damage identification. GANs are another type of deep-learning-based unsupervised model that have been broadly used for image processing purposes [35][36][37]. ey have recently been used in the field of SHM to generate new data [38][39][40] or identify anomalies in data for damage detection [41].…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Network for Damage Identification in Civil Structures

Rastin

Amiri

Darvishan

2021

Shock and Vibration

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In recent years, many efforts have been made to develop efficient deep-learning-based structural health monitoring (SHM) methods. Most of the proposed methods employ supervised algorithms that require data from different damaged states of a structure in order to monitor its health conditions. As such data are not usually available for real civil structures, using supervised algorithms for the health monitoring of these structures might be impracticable. This paper presents a novel two-stage technique based on generative adversarial networks (GANs) for unsupervised SHM and damage identification. In the first stage, a deep convolutional GAN (DCGAN) is used to detect and quantify structural damages; the detected damages are then localized in the second stage using a conditional GAN (CGAN). Raw acceleration signals from a monitored structure are used for this purpose, and the networks are trained by only the intact state data of the structure. The proposed method is validated through applications on the numerical model of a bridge health monitoring (BHM) benchmark structure, an experimental steel structure located at Qatar University, and the full-scale Tianjin Yonghe Bridge.

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Human Face Sketch to RGB Image with Edge Optimization and Generative Adversarial Networks

Cited by 20 publications

References 23 publications

[Retracted] Study on 3D Clothing Color Application Based on Deep Learning‐Enabled Macro‐Micro Adversarial Network and Human Body Modeling

[Retracted] Study on 3D Clothing Color Application Based on Deep Learning‐Enabled Macro‐Micro Adversarial Network and Human Body Modeling

Cognitive Covert Traffic Synthesis Method Based on Generative Adversarial Network

Generative Adversarial Network for Damage Identification in Civil Structures

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