PeaceGAN: A GAN-Based Multi-Task Learning Method for SAR Target Image Generation with a Pose Estimator and an Auxiliary Classifier

Although generative adversarial networks (GANs) are successfully applied to diverse fields, training GANs on synthetic aperture radar (SAR) data is a challenging task due to speckle noise. On the one hand, in a learning perspective of human perception, it is natural to learn a task by using information from multiple sources. However, in the previous GAN works on SAR image generation, information on target classes has only been used. Due to the backscattering characteristics of SAR signals, the structures of SAR images are strongly dependent on their pose angles. Nevertheless, the pose angle information has not been incorporated into GAN models for SAR images. In this paper, we propose a novel GAN-based multi-task learning (MTL) method for SAR target image generation, called PeaceGAN, that has two additional structures, a pose estimator and an auxiliary classifier, at the side of its discriminator in order to effectively combine the pose and class information via MTL. Extensive experiments showed that the proposed MTL framework can help the PeaceGAN’s generator effectively learn the distributions of SAR images so that it can better generate the SAR target images more faithfully at intended pose angles for desired target classes in comparison with the recent state-of-the-art methods.

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Enforcing perceptual consistency on Generative Adversarial Networks by using the Normalised Laplacian Pyramid Distance

Proceedings of the Northern Lights Deep Learning Workshop ◽

10.7557/18.5124 ◽

2020 ◽

Vol 1 ◽

pp. 6

Author(s):

Alexander Hepburn ◽

Valero Laparra ◽

Ryan McConville ◽

Raul Santos-Rodriguez

Keyword(s):

Visual Inspection ◽

Human Perception ◽

Generative Adversarial Networks ◽

Perceptual Similarity ◽

Image Generation ◽

Laplacian Pyramid ◽

Training Process ◽

Adversarial Networks ◽

Image Translation ◽

Do So

In recent years there has been a growing interest in image generation through deep learning. While an important part of the evaluation of the generated images usually involves visual inspection, the inclusion of human perception as a factor in the training process is often overlooked. In this paper we propose an alternative perceptual regulariser for image-to-image translation using conditional generative adversarial networks (cGANs). To do so automatically (avoiding visual inspection), we use the Normalised Laplacian Pyramid Distance (NLPD) to measure the perceptual similarity between the generated image and the original image. The NLPD is based on the principle of normalising the value of coefficients with respect to a local estimate of mean energy at different scales and has already been successfully tested in different experiments involving human perception. We compare this regulariser with the originally proposed L1 distance and note that when using NLPD the generated images contain more realistic values for both local and global contrast.

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Application of Deep Learning in Fault Diagnosis of Rotating Machinery

Processes ◽

10.3390/pr9060919 ◽

2021 ◽

Vol 9 (6) ◽

pp. 919

Author(s):

Wanlu Jiang ◽

Chenyang Wang ◽

Jiayun Zou ◽

Shuqing Zhang

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Fault Diagnosis ◽

Rotating Machinery ◽

Generative Adversarial Networks ◽

Extraction Ability ◽

One Dimensional ◽

Adversarial Networks ◽

Diagnosis Model ◽

The One

The field of mechanical fault diagnosis has entered the era of “big data”. However, existing diagnostic algorithms, relying on artificial feature extraction and expert knowledge are of poor extraction ability and lack self-adaptability in the mass data. In the fault diagnosis of rotating machinery, due to the accidental occurrence of equipment faults, the proportion of fault samples is small, the samples are imbalanced, and available data are scarce, which leads to the low accuracy rate of the intelligent diagnosis model trained to identify the equipment state. To solve the above problems, an end-to-end diagnosis model is first proposed, which is an intelligent fault diagnosis method based on one-dimensional convolutional neural network (1D-CNN). That is to say, the original vibration signal is directly input into the model for identification. After that, through combining the convolutional neural network with the generative adversarial networks, a data expansion method based on the one-dimensional deep convolutional generative adversarial networks (1D-DCGAN) is constructed to generate small sample size fault samples and construct the balanced data set. Meanwhile, in order to solve the problem that the network is difficult to optimize, gradient penalty and Wasserstein distance are introduced. Through the test of bearing database and hydraulic pump, it shows that the one-dimensional convolution operation has strong feature extraction ability for vibration signals. The proposed method is very accurate for fault diagnosis of the two kinds of equipment, and high-quality expansion of the original data can be achieved.

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S2I-Bird: Sound-to-Image Generation of Bird Species using Generative Adversarial Networks

2020 25th International Conference on Pattern Recognition (ICPR) ◽

10.1109/icpr48806.2021.9412721 ◽

2021 ◽

Author(s):

Joo Yong Shim ◽

Joongheon Kim ◽

Jong-Kook Kim

Keyword(s):

Bird Species ◽

Generative Adversarial Networks ◽

Image Generation ◽

Adversarial Networks

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Automatic Target Recognition for Low Resolution Foliage Penetrating SAR Images Using CNNs and GANs

Remote Sensing ◽

10.3390/rs13040596 ◽

2021 ◽

Vol 13 (4) ◽

pp. 596

Author(s):

David Vint ◽

Matthew Anderson ◽

Yuhao Yang ◽

Christos Ilioudis ◽

Gaetano Di Caterina ◽

...

Keyword(s):

Target Recognition ◽

Automatic Target Recognition ◽

Generative Adversarial Networks ◽

Low Resolution ◽

Sar Images ◽

Adversarial Networks ◽

Technological Advances ◽

Dataset Size ◽

Resolution Imaging ◽

High Level

In recent years, the technological advances leading to the production of high-resolution Synthetic Aperture Radar (SAR) images has enabled more and more effective target recognition capabilities. However, high spatial resolution is not always achievable, and, for some particular sensing modes, such as Foliage Penetrating Radars, low resolution imaging is often the only option. In this paper, the problem of automatic target recognition in Low Resolution Foliage Penetrating (FOPEN) SAR is addressed through the use of Convolutional Neural Networks (CNNs) able to extract both low and high level features of the imaged targets. Additionally, to address the issue of limited dataset size, Generative Adversarial Networks are used to enlarge the training set. Finally, a Receiver Operating Characteristic (ROC)-based post-classification decision approach is used to reduce classification errors and measure the capability of the classifier to provide a reliable output. The effectiveness of the proposed framework is demonstrated through the use of real SAR FOPEN data.

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Fractional Wavelet-Based Generative Scattering Networks

Frontiers in Neurorobotics ◽

10.3389/fnbot.2021.752752 ◽

2021 ◽

Vol 15 ◽

Author(s):

Jiasong Wu ◽

Xiang Qiu ◽

Jing Zhang ◽

Fuzhi Wu ◽

Youyong Kong ◽

...

Keyword(s):

Dimensionality Reduction ◽

Reduction Method ◽

Gaussian White Noise ◽

Principal Component ◽

Experimental Results ◽

Generative Adversarial Networks ◽

Image Generation ◽

Adversarial Networks ◽

Dimensionality Reduction Method

Generative adversarial networks and variational autoencoders (VAEs) provide impressive image generation from Gaussian white noise, but both are difficult to train, since they need a generator (or encoder) and a discriminator (or decoder) to be trained simultaneously, which can easily lead to unstable training. To solve or alleviate these synchronous training problems of generative adversarial networks (GANs) and VAEs, researchers recently proposed generative scattering networks (GSNs), which use wavelet scattering networks (ScatNets) as the encoder to obtain features (or ScatNet embeddings) and convolutional neural networks (CNNs) as the decoder to generate an image. The advantage of GSNs is that the parameters of ScatNets do not need to be learned, while the disadvantage of GSNs is that their ability to obtain representations of ScatNets is slightly weaker than that of CNNs. In addition, the dimensionality reduction method of principal component analysis (PCA) can easily lead to overfitting in the training of GSNs and, therefore, affect the quality of generated images in the testing process. To further improve the quality of generated images while keeping the advantages of GSNs, this study proposes generative fractional scattering networks (GFRSNs), which use more expressive fractional wavelet scattering networks (FrScatNets), instead of ScatNets as the encoder to obtain features (or FrScatNet embeddings) and use similar CNNs of GSNs as the decoder to generate an image. Additionally, this study develops a new dimensionality reduction method named feature-map fusion (FMF) instead of performing PCA to better retain the information of FrScatNets,; it also discusses the effect of image fusion on the quality of the generated image. The experimental results obtained on the CIFAR-10 and CelebA datasets show that the proposed GFRSNs can lead to better generated images than the original GSNs on testing datasets. The experimental results of the proposed GFRSNs with deep convolutional GAN (DCGAN), progressive GAN (PGAN), and CycleGAN are also given.

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