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59th Annual Conference of the Society-of-Instrument-and-Control-Engineers-of-Japan (SICE)

作者： Khine, Win Shwe Sin Siritanawan, Prarinya Kotani, Kazunori Japan Adv Inst Sci & Technol Sch Informat Sci Nomi Ishikawa Japan

ISBN: (纸本)9781728110899

Facial expressions of human emotions play an essential role in gaining insights into human cognition. They are crucial for designing human-computer interaction models. Although human emotional states are not limited to basic emotions such as happiness, sadness, anger, fear, disgust, and surprise, most of the current researches are focusing on those basic emotions. In this study, we proposed a new methodology to create facial expressions of compound emotions that evolve from combining those of basic emotions. In our experiments, we train our proposed model, namely Emotion generative adversarial Network (EmoGANs), in both unsupervised and supervised manners to improve the quality of generated images. To demonstrate the efficiency of the proposed method, we use the Extended Cohn-Kanade Dataset (CK+) and Japanese Female Facial Expressions Dataset (JAFFE) as inputs and visualize the generated images from our proposed EmoGANs. In the experiment, our proposed methodology can manipulate basic facial expressions to create facial expressions of compound emotions.

关键词： Compound Emotions deep convolutional generative adversarial networks Facial Expressions deep-learning

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DCGANs for Realistic Breast Mass Augmentation in X-ray Mammography

DCGANs for Realistic Breast Mass Augmentation in X-ray Mammo...

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Conference on Medical Imaging - Computer-Aided Diagnosis

作者： Alyafi, Basel Diaz, Oliver Marti, Robert Univ Girona Comp Vis & Robot Inst Girona Spain Univ Barcelona Dept Math & Comp Sci Barcelona Spain

ISBN: (纸本)9781510633964

Early detection has a major contribution to the curability of breast cancer, and using mammographic images, this can be achieved non-invasively. Supervised deep learning, the dominant computer-aided detection (CADe) tool currently, has played a great role in object detection in computer vision, but it suffers from a limiting property: the need of a large amount of labelled data. This becomes stricter when it comes to medical datasets which require high-cost and time-consuming annotations. Furthermore, medical datasets are usually imbalanced, a condition that often hinders classifiers performance. The aim of this paper is to learn the distribution of the minority class to synthesise new samples in order to improve lesion detection in mammography. deep convolutional generative adversarial networks (DCGANs) can efficiently generate breast masses. They were trained on increasing-size subsets of a mammographic dataset and used to generate diverse and realistic breast masses. The effect of including the generated images and/or applying horizontal and vertical flipping was tested in an environment where an imbalanced dataset (ratio of 1:10) of masses and normal tissue patches was classified using a fully-convolutional network. A maximum of 0.09 improvement of F1 score was reported by using DCGANs along with flipping augmentation in contrast to using the original images. We show that DCGANs can be used for synthesising photo-realistic breast mass patches with a considerable diversity. It is demonstrated that appending synthetic images in this environment, along with flipping, outperforms the traditional augmentation method of flipping solely, offering faster improvements as a function of the training set size.

关键词： breast cancer computer-aided detection deep learning deep convolutional generative adversarial networks fully-convolutional networks data augmentation

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The Structure-Mechanics Relationship of Bamboo-Epidermis and Inspired Composite Design by Artificial Intelligence (Adv. Mater. 22/2025)

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Advanced Materials 2025年第22期37卷

作者： Zhao Qin Aymeric Pierre Destree Laboratory for Multiscale Material Modelling Syracuse University 151L Link Hall Syracuse NY 13244 USA Department of Civil and Environmental Engineering Syracuse University 151L Link Hall Syracuse NY 13244 USA The BioInspired Institute Syracuse University Syracuse NY 13244 USA

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A Simple Recurrent Unit Model Based Intrusion Detection System With DCGAN

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IEEE ACCESS 2019年 7卷 83286-83296页

作者： Yang, Jin Li, Tao Liang, Gang He, Wenbo Zhao, Yue Sichuan Univ Coll Cyber Secur Dept Comp & Software Chengdu 610017 Sichuan Peoples R China McMaster Univ Sci & Technol Commun Secur Lab Hamilton ON L8S 4L8 Canada Sci & Technol Commun Secur Lab Chengdu 610041 Sichuan Peoples R China

Due to the complex and time-varying network environments, traditional methods are difficult to extract accurate features of intrusion behavior from the high-dimensional data samples and process the high-volume of these data efficiently. Even worse, the network intrusion samples are submerged into a large number of normal data packets, which leads to insufficient samples for model training;therefore it is accompanied by high false detection rates. To address the challenge of unbalanced positive and negative learning samples, we propose using deep convolutional generative adversarial networks (DCGAN), which allows features to be extracted directly from the rawdata, and then generates new training-sets by learning from the rawdata. Given the fact that the attack samples are usually intra-dependent time sequence data, we apply long short-term memory (LSTM) to automatically learn the features of network intrusion behaviors. However, it is hard to parallelize the learning/training of the LSTM network, since the LSTM algorithm depends on the result of the previous moment. To remove such dependency and enable intrusion detection in real time, we propose a simple recurrent unit based (SRU)-based model. The proposed model was verified by extensive experiments on the benchmark datasets KDD'99 and NSL-KDD, which effectively identifies normal and abnormal network activities. It achieves 99.73% accuracy on the KDD'99 dataset and 99.62% on the NSL-KDD dataset.

关键词： Network security deep learning intrusion detection system (IDS) simple recurrent unit deep convolutional generative adversarial networks

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DA-DCGAN: An Effective Methodology for DC Series Arc Fault Diagnosis in Photovoltaic Systems

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IEEE ACCESS 2019年 7卷 45831-45840页

作者： Lu, Shibo Sirojan, Tharmakulasingam Phung, B. T. Zhang, Daming Ambikairajah, Eliathamby Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

DC arc faults, especially series arcing, can occur in photovoltaic (PV) systems and pose a challenging detection and protection problem. Machine learning-based methods are increasingly being used for fault diagnosis applications. However, the performance of such detection algorithms will degrade because of variations between the source domain data used during the development and the target domain data encountered in operation of the field. Furthermore, the fault's data in the target domain for model training are usually not available. In this paper, domain adaptation combined with deep convolutional generative adversarial network (DA-DCGAN)-based methodology is proposed, where DA-DCGAN first learns an intelligent normal-to-arcing transformation from the source-domain data. Then by generating dummy arcing data with the learned transformation using the normal data from the target domain and employing domain adaptation, a robust and reliable fault diagnosis scheme can be achieved for the target domain. The PV loop current is framed and arranged into a 2D matrix as input for cross-domain DC series arc fault diagnosis. The system is validated offline using pre-recorded PV loop current data from a real 1.5-kW grid-connected rooftop PV system. Also, the proposed method is implemented in an embedded system and tested in real-time according to UL-1699B standard. The experimental results clearly demonstrate benefits of DA-DCGAN and confirm the effectiveness of the proposed methodology for practical PV applications.

关键词： deep learning domain adaptation deep convolutional generative adversarial networks DC series arc fault diagnosis photovoltaic systems

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Cardiac diffusion tensor imaging simulation based on deep convolutional generative adversarial network 14

Cardiac diffusion tensor imaging simulation based on deep co...

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14th IEEE International Conference on Signal Processing (ICSP)

作者： Liu, Bin Wang, Lihui Zhang, Jian Cheng, Xinyu Yang, Feng Huang, Jianping Zhu, Yuemin Guizhou Univ Sch Comp Sci & Technol Key Lab Intelligent Med Image Anal & Precise Diag Guiyang Guizhou Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Peoples R China Northeast Forestry Univ Coll Mech & Elect Engn Harbin Heilongjiang Peoples R China Univ Lyon CNRS INSERM INSA LyonCREATISUMR 5220U1206 F-69621 Villeurbanne France

ISBN: (纸本)9781538646731

Myocardial fiber structure is related to heart function, the research of which is therefore of great fundamental and clinical importance. Diffusion magnetic resonance imaging (dMRI) is the most promising method for non-invasively investigating in vivo cardiac fiber structures. However, the acquisition of dMRI images for in vivo heart is difficult due to the motion influence caused by heartbeat and breathing. To deal with the acquisition problems, we propose dMRI simulation for cardiac fiber structure using deep convolutional generative adversarial networks (DCGAN). As the first attempt, we simulate dMRI images of ex vivo hearts. Diffusion-weighted (DW) images of cardiac fibers are first generated by a DCGAN network that is trained with the acquired DW images, and then the diffusion tensor images are calculated for both acquired and simulated data, from which the cardiac fiber orientations, fractional anisotropy (FA) and mean diffusivity (MD) derived from acquisitions and simulations are finally compared. The experimental results show that the simulated DW images and the corresponding fiber orientations are very close to the real ones, and that the simulated FA and MD maps have the same distribution as the real ones but with some shift in the mean values.

关键词： Myocardial magnetic resonance diffusion imaging simulation deep convolutional generative adversarial networks Diffusion tensor imaging

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SINOGRAM IMAGE COMPLETION FOR LIMITED ANGLE TOMOGRAPHY WITH generative adversarial networks 26

SINOGRAM IMAGE COMPLETION FOR LIMITED ANGLE TOMOGRAPHY WITH ...

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26th IEEE International Conference on Image Processing (ICIP)

作者： Yoo, Seunghwan Yang, Xiaogang Wolfman, Mark Gursoy, Doga Katsaggelos, Aggelos K. Northwestern Univ Dept Elect Engn & Comp Sci Evanston IL 60208 USA Argonne Natl Lab Adv Photon Source Argonne IL 60439 USA

ISBN: (纸本)9781538662496

In this paper, we present a novel approach based on deep neural network for solving the limited angle tomography problem. The limited angle views in tomography cause severe artifacts in the tomographic reconstruction. We use deep convolutional generative adversarial networks (DCGAN) to fill in the missing information in the sinogram domain. By using the continuity loss and the two-ends method, the image completion in the sinogram domain is done effectively, resulting in high quality reconstructions with fewer artifacts. The sinogram completion method can be applied to different problems such as ring artifact removal and truncated tomography problems.

关键词： Limited angle tomography sinogram image completion deep convolutional generative adversarial networks

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Digital Recognition of Street View House Numbers Based on DCGAN 19

Digital Recognition of Street View House Numbers Based on DC...

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2nd International Conference on Image and Graphics Processing (ICIGP) / 5th International Conference on Virtual Reality

作者： Zhong, Juping Gao, Jing Chen, Rongjun Li, Jun Guangdong Polytech Normal Univ Coll Comp Sci Guangzhou 510665 Guangdong Peoples R China Guangdong Hengdian Informat Technol Co Ltd Guangzhou 510635 Guangdong Peoples R China

ISBN: (纸本)9781450360920

deep learning algorithms have surpassed human resolution in applications such as face recognition and object classification. However, it can only produce very blurred, lack of details of the image. generative adversarial Network is a game training of minimax antagonism between generator G and discriminator D, and ultimately achieves Nash equilibrium. We use deep convolutional GAN that recognizes sequence numbers and without split characters. First we use convolution network to extract character features. Second we construct a convolution neural network to recognize digits of natural scene house number. DCGAN is used to improve the resolution of the number of fuzzy houses, so as to extract more abundant data features in data set training. It can better recognize the numbers in the natural street.

关键词： Image processing machine learning deep convolutional generative adversarial networks street view house numbers

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Unsupervised Facial Image De-occlusion with Optimized deep generative Models 8

Unsupervised Facial Image De-occlusion with Optimized Deep G...

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8th International Conference on Image Processing Theory, Tools and Applications (IPTA)

作者： Xu, Lei Zhang, Honglei Raitoharju, Jenni Gabbouj, Moncef Tampere Univ Technol Signal Proc Lab Tampere Finland

ISBN: (纸本)9781538664285

In recent years, generative adversarial networks (GANs) or various types of Auto-Encoders (AEs) have gained attention on facial image de-occlusion and/or in-painting tasks. In this paper, we propose a novel unsupervised technique to remove occlusion from facial images and complete the occluded parts simultaneously with optimized deep convolutional generative adversarial networks (DCGANs) in an iterative way. Generally, GANs, as generative models, can estimate the distribution of images using a generator and a discriminator. DCGANs, as its variant, are proposed to conquer its instability during training. Existing facial image in-painting methods manually define a block of pixels as the missing part and the potential content of this block is semantically generated using generative models, such as GANs or AEs. In our method, a mask is inferred from an occluded facial image using a novel loss function, and then this mask is utilized to in-paint the occlusions automatically by pre-trained DCGANs. We evaluate the performance of our method on facial images with various occlusions, such as sunglasses and scarves. The experiments demonstrate that our method can effectively detect certain kinds of occlusions and complete the occluded parts in an unsupervised manner.

关键词： generative adversarial networks deep convolutional generative adversarial networks Facial Image De-occlusion Facial Image Completion

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Digital Recognition of Street View House Numbers Based on DCGAN

Digital Recognition of Street View House Numbers Based on DC...

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2019第二届图像与图形处理国际会议

作者： Juping Zhong Jing Gao Rongjun Chen Jun Li College of Computer Science Guangdong Polytechnic Normal University Guangdong Hengdian Information Technology Co. Ltd.

关键词： Image processing machine learning deep convolutional generative adversarial networks street view house numbers

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