Improved Boundary Equilibrium Generative Adversarial Networks

Li, Yanchun; Ouyang, Wanli

doi:10.1109/access.2018.2804278

Cited by 42 publications

(18 citation statements)

References 2 publications

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“…Finally, according to the convergence of discriminator loss and generator loss [10], the training end time point is determined, and the training model is obtained as the basis for processing the unprepared area of the bright area of the image again (Figure 2). e innovation of this work is to use the RGB information data after defogging of the DCP method and use the RGB information data of the fog-less image as the counter training; it has solved the unavoidable confusion of defogging distortion of the bright part of DCP effectively [11,12].…”

Section: Our Methodsmentioning

confidence: 99%

Single Image Defogging Algorithm Based on Conditional Generative Adversarial Network

Shen

Zhang

2020

Mathematical Problems in Engineering

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Outside the house, images taken using a phone in foggy weather are not suitable for automation due to low contrast. Usually, it is revised in the dark channel prior (DCP) method (K. He et al. 2009), but the non-sky bright area exists due to mistakes in the removal. In this paper, we propose an algorithm, defog-based generative adversarial network (DbGAN). We use generative adversarial network (GAN) for training and embed target map (TM) in the anti-network generator, only the part of bright area layer of image, in local attention model image training and testing in deep learning, and the effective processing of the wrong removal part is achieved, thus better restoring the defog image. Then, the DCP method obtains a good defog visual effect, and the evaluation index peak signal-to-noise ratio (PSNR) is used to make a judgment; the simulation result is consistent with the visual effect. We proved the DbGAN is a practical import of target map in the GAN. The algorithm is used defogging in the highlighted area is well realized, which makes up for the shortcomings of the DCP algorithm.

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Section: Our Methodsmentioning

confidence: 99%

Single Image Defogging Algorithm Based on Conditional Generative Adversarial Network

Shen

Zhang

2020

Mathematical Problems in Engineering

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“…Eventually, the training will reach Nash equilibrium, where the Discriminator will be unable to differentiate between two distributions, i.e., D(x) = 1/2. Since this equilibrium is very tough to find, there are many research papers [115], [116] for cracking this issue [114] [117]. Fig.…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

2020

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A smart factory is a highly digitized and connected production facility that relies on smart manufacturing. Additionally, artificial intelligence is the core technology of smart factories. The use of machine learning and deep learning algorithms has produced fruitful results in many fields like image processing, speech recognition, fault detection, object detection, or medical sciences. With the increment in the use of smart machinery, the faults in the machinery equipment are expected to increase. Machinery fault detection and diagnosis through various deep learning algorithms has increased day by day. Many types of research have been done and published using both open-source and closed-source datasets, implementing the deep learning algorithms. Out of many publicly available datasets, Case Western Reserve University (CWRU) bearing dataset has been widely used to detect and diagnose machinery bearing fault and is accepted as a standard reference for validating the models. This paper summarizes the recent works which use the CWRU bearing dataset in machinery fault detection and diagnosis employing deep learning algorithms. We have reviewed the published works and presented the working algorithm, result, and other necessary details in this paper. This paper, we believe, can be of good help for future researchers to start their work on machinery fault detection and diagnosis using the CWRU dataset. INDEX TERMS Bearing, deep learning, machine learning, machinery fault detection and diagnosis, CWRU dataset.

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“…To solve problems of the original GAN, such as gradient disappearance, unstable training, and poor diversity, many new GAN models have been proposed to increase the stability and to improve qualities of generated results [56], [43]. In this section, we will introduce the evolution of GAN models, including deep convolutional generative adversarial network (DCGAN) [20], conditional GAN (CGAN) [28], Wasserstein GAN (WGAN) [25], WGAN with gradient penalty (WGAN-GP) [31], Energy-Based GAN (EBGAN) [30], Boundary Equilibrium GAN (BEGAN) [8], Information GAN (InfoGAN) [29], Least Squares GAN (LSGAN) [32], Auxiliary Classifier GAN (ACGAN) [6], Degenerate avoided GAN (DRAGAN) [33], Spectral Normalization GAN (SNGAN) [34], Jacobian Regularization GAN (JR-GAN) [36], CapsGAN [37], Banach Wasserstein GAN (BWGAN) [38], Decoder-Encoder GAN (DEGAN) [39].…”

Section: The Evolution Of Gan Modelmentioning

confidence: 99%

Recent Advances of Generative Adversarial Networks in Computer Vision

et al. 2019

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The appearance of generative adversarial networks (GAN) provides a new approach and framework for computer vision. Compared with traditional machine learning algorithms, GAN works via adversarial training concept and is more powerful in both feature learning and representation. GAN also exhibits some problems, such as non-convergence, model collapse, and uncontrollability due to high degree of freedom. How to improve the theory of GAN and apply it to computer-vision-related tasks have now attracted much research efforts. In this paper, recently proposed GAN models and their applications in computer vision are systematically reviewed. In particular, we firstly survey the history and development of generative algorithms, the mechanism of GAN, its fundamental network structures, and theoretical analysis of the original GAN. Classical GAN algorithms are then compared comprehensively in terms of the mechanism, visual results of generated samples, and Frechet Inception Distance. These networks are further evaluated from network construction, performance, and applicability aspects by extensive experiments conducted over public datasets. After that, several typical applications of GAN in computer vision, including high-quality samples generation, style transfer, and image translation, are examined. Finally, some existing problems of GAN are summarized and discussed and potential future research topics are forecasted. INDEX TERMS Deep learning, generative adversarial networks (GAN), computer vision (CV), image generation, style transfer, image inpainting.

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Improved Boundary Equilibrium Generative Adversarial Networks

Cited by 42 publications

References 2 publications

Single Image Defogging Algorithm Based on Conditional Generative Adversarial Network

Single Image Defogging Algorithm Based on Conditional Generative Adversarial Network

Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

Recent Advances of Generative Adversarial Networks in Computer Vision

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