Xin Liu^*, Xiao Chen

Journal of Information Hiding and Privacy Protection, Vol.2, No.2, pp. 87-94, 2020, DOI:10.32604/jihpp.2020.09839

Abstract In recent years, with the rapid growth of generative adversarial networks (GANs), a photo-realistic face can be easily generated from a random vector. Moreover, the faces generated by advanced GANs are very realistic. It is reasonable to acknowledge that even a well-trained viewer has difficulties to distinguish artificial from real faces. Therefore, detecting the face generated by GANs is a necessary work. This paper mainly introduces some methods to detect GAN-generated fake faces, and analyzes the advantages and disadvantages of these models based on the network structure and evaluation indexes, and the results obtained in the respective data sets. On… More >

Seo Woo Han, Doug Young Suh^*

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 213-228, 2021, DOI:10.32604/cmc.2020.012223

Abstract Inpainting has been continuously studied in the field of computer vision. As artificial intelligence technology developed, deep learning technology was introduced in inpainting research, helping to improve performance. Currently, the input target of an inpainting algorithm using deep learning has been studied from a single image to a video. However, deep learning-based inpainting technology for panoramic images has not been actively studied. We propose a 360-degree panoramic image inpainting method using generative adversarial networks (GANs). The proposed network inputs a 360-degree equirectangular format panoramic image converts it into a cube map format, which has relatively little distortion and uses it… More >

Xiaoyan Chen, Jiahuan Chen^*, Zhongcheng Sha

Journal of New Media, Vol.2, No.2, pp. 61-77, 2020, DOI:10.32604/jnm.2020.010062

Abstract Aiming at the problem that the detection effect of traditional edge detection algorithm is not good, and the problem that the existing edge detection algorithm based on convolution network cannot solve the thick edge problem from the model itself, this paper proposes a new edge detection method based on the generative adversarial network. The confrontation network consists of generator network and discriminator network, generator network is composed of U-net network and discriminator network is composed of five-layer convolution network. In this paper, we use BSDS500 training data set to train the model. Finally, several images are randomly selected from BSDS500… More >

Kui Fu¹, Jiansheng Peng^{1, 2, *}, Hanxiao Zhang², Xiaoliang Wang³, Frank Jiang⁴

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1977-1997, 2020, DOI:10.32604/cmc.2020.09882

Abstract Single image super resolution (SISR) is an important research content in the field of computer vision and image processing. With the rapid development of deep neural networks, different image super-resolution models have emerged. Compared to some traditional SISR methods, deep learning-based methods can complete the superresolution tasks through a single image. In addition, compared with the SISR methods using traditional convolutional neural networks, SISR based on generative adversarial networks (GAN) has achieved the most advanced visual performance. In this review, we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics. Then, we review the… More >

Ming Zhao¹, Xinhong Liu¹, Xin Yao^{1, *}, Kun He²

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1601-1614, 2020, DOI:10.32604/cmc.2020.09754

Abstract Although there has been a great breakthrough in the accuracy and speed of super-resolution (SR) reconstruction of a single image by using a convolutional neural network, an important problem remains unresolved: how to restore finer texture details during image super-resolution reconstruction? This paper proposes an Enhanced Laplacian Pyramid Generative Adversarial Network (ELSRGAN), based on the Laplacian pyramid to capture the high-frequency details of the image. By combining Laplacian pyramids and generative adversarial networks, progressive reconstruction of super-resolution images can be made, making model applications more flexible. In order to solve the problem of gradient disappearance, we introduce the Residual-in-Residual Dense… More >

Tong Li¹, Shibin Zhang^{1, *}, Jinyue Xia²

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 401-438, 2020, DOI:10.32604/cmc.2020.010551

Abstract Generative adversarial network (GAN) is one of the most promising methods for unsupervised learning in recent years. GAN works via adversarial training concept and has shown excellent performance in the fields image synthesis, image super-resolution, video generation, image translation, etc. Compared with classical algorithms, quantum algorithms have their unique advantages in dealing with complex tasks, quantum machine learning (QML) is one of the most promising quantum algorithms with the rapid development of quantum technology. Specifically, Quantum generative adversarial network (QGAN) has shown the potential exponential quantum speedups in terms of performance. Meanwhile, QGAN also exhibits some problems, such as barren… More >

Xin Li^1,2, Yanchun Liang^1,2, Minghao Zhao^1,2, Chong Wang^1,2,3, Yu Jiang^1,2,*

CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1073-1085, 2019, DOI:10.32604/cmc.2019.05929

Abstract In recent years, extreme weather events accompanying the global warming have occurred frequently, which brought significant impact on national economic and social development. The ocean is an important member of the climate system and plays an important role in the occurrence of climate anomalies. With continuous improvement of sensor technology, we use sensors to acquire the ocean data for the study on resource detection and disaster prevention, etc. However, the data acquired by the sensor is not enough to be used directly by researchers, so we use the Generative Adversarial Network (GAN) to enhance the ocean data. We use GAN… More >

Chuanlong Li^1,2,*, Yumeng Jiang³, Marta Cheslyar¹

CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 313-324, 2018, DOI: 10.3970/cmc.2018.03950

Abstract Deep neural network has proven to be very effective in computer vision fields. Deep convolutional network can learn the most suitable features of certain images without specific measure functions and outperform lots of traditional image processing methods. Generative adversarial network (GAN) is becoming one of the highlights among these deep neural networks. GAN is capable of generating realistic images which are imperceptible to the human vision system so that the generated images can be directly used as intermediate medium for many tasks. One promising application of using GAN generated images would be image concealing which requires the embedded image looks… More >

Ya Tu¹, Yun Lin¹, Jin Wang^2,3,*, Jeong-Uk Kim⁴

CMC-Computers, Materials & Continua, Vol.55, No.2, pp. 243-254, 2018, DOI:10.3970/cmc.2018.01755

Abstract Deep Learning (DL) is such a powerful tool that we have seen tremendous success in areas such as Computer Vision, Speech Recognition, and Natural Language Pro-cessing. Since Automated Modulation Classification (AMC) is an important part in Cognitive Radio Networks, we try to explore its potential in solving signal modula-tion recognition problem. It cannot be overlooked that DL model is a complex mod-el, thus making them prone to over-fitting. DL model requires many training data to combat with over-fitting, but adding high quality labels to training data manually is not always cheap and accessible, especially in real-time system, which may counter… More >

Daojian Zeng^1,3, Yuan Dai^1,3, Feng Li^1,3, R. Simon Sherratt², Jin Wang^3,*

CMC-Computers, Materials & Continua, Vol.55, No.1, pp. 121-136, 2018, DOI:10.3970/cmc.2018.055.121

Abstract Recently, many researchers have concentrated on using neural networks to learn features for Distant Supervised Relation Extraction (DSRE). These approaches generally use a softmax classifier with cross-entropy loss, which inevitably brings the noise of artificial class NA into classification process. To address the shortcoming, the classifier with ranking loss is employed to DSRE. Uniformly randomly selecting a relation or heuristically selecting the highest score among all incorrect relations are two common methods for generating a negative class in the ranking loss function. However, the majority of the generated negative class can be easily discriminated from positive class and will contribute… More >