State-of-the-Art Approaches for Image Deconvolution Problems, including Modern Deep Learning Architectures

In modern digital microscopy, deconvolution methods are widely used to eliminate a number of image defects and increase resolution. In this review, we have divided these methods into classical, deep learning-based, and optimization-based methods. The review describes the major architectures of neural networks, such as convolutional and generative adversarial networks, autoencoders, various forms of recurrent networks, and the attention mechanism used for the deconvolution problem. Special attention is paid to deep learning as the most powerful and flexible modern approach. The review describes the major architectures of neural networks used for the deconvolution problem. We describe the difficulties in their application, such as the discrepancy between the standard loss functions and the visual content and the heterogeneity of the images. Next, we examine how to deal with this by introducing new loss functions, multiscale learning, and prior knowledge of visual content. In conclusion, a review of promising directions and further development of deconvolution methods in microscopy is given.

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Deep learning in bioinformatics: introduction, application, and perspective in big data era

10.1101/563601 ◽

2019 ◽

Cited By ~ 9

Author(s):

Yu Li ◽

Chao Huang ◽

Lizhong Ding ◽

Zhongxiao Li ◽

Yijie Pan ◽

...

Keyword(s):

Neural Networks ◽

Big Data ◽

Deep Learning ◽

Generative Adversarial Networks ◽

Great Success ◽

Research Directions ◽

Adversarial Networks ◽

Variational Autoencoder ◽

The Common ◽

Graph Neural Networks

AbstractDeep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics. With the advances of the big data era in biology, it is foreseeable that deep learning will become increasingly important in the field and will be incorporated in vast majorities of analysis pipelines. In this review, we provide both the exoteric introduction of deep learning, and concrete examples and implementations of its representative applications in bioinformatics. We start from the recent achievements of deep learning in the bioinformatics field, pointing out the problems which are suitable to use deep learning. After that, we introduce deep learning in an easy-to-understand fashion, from shallow neural networks to legendary convolutional neural networks, legendary recurrent neural networks, graph neural networks, generative adversarial networks, variational autoencoder, and the most recent state-of-the-art architectures. After that, we provide eight examples, covering five bioinformatics research directions and all the four kinds of data type, with the implementation written in Tensorflow and Keras. Finally, we discuss the common issues, such as overfitting and interpretability, that users will encounter when adopting deep learning methods and provide corresponding suggestions. The implementations are freely available at https://github.com/lykaust15/Deep_learning_examples.

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LSUN-Stanford Car Dataset: Enhancing Large-Scale Car Image Datasets Using Deep Learning for Usage in GAN Training

Applied Sciences ◽

10.3390/app10144913 ◽

2020 ◽

Vol 10 (14) ◽

pp. 4913

Author(s):

Tin Kramberger ◽

Božidar Potočnik

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Large Scale ◽

Generative Adversarial Networks ◽

Adversarial Networks ◽

Image Dataset ◽

Image Datasets

Currently there is no publicly available adequate dataset that could be used for training Generative Adversarial Networks (GANs) on car images. All available car datasets differ in noise, pose, and zoom levels. Thus, the objective of this work was to create an improved car image dataset that would be better suited for GAN training. To improve the performance of the GAN, we coupled the LSUN and Stanford car datasets. A new merged dataset was then pruned in order to adjust zoom levels and reduce the noise of images. This process resulted in fewer images that could be used for training, with increased quality though. This pruned dataset was evaluated by training the StyleGAN with original settings. Pruning the combined LSUN and Stanford datasets resulted in 2,067,710 images of cars with less noise and more adjusted zoom levels. The training of the StyleGAN on the LSUN-Stanford car dataset proved to be superior to the training with just the LSUN dataset by 3.7% using the Fréchet Inception Distance (FID) as a metric. Results pointed out that the proposed LSUN-Stanford car dataset is more consistent and better suited for training GAN neural networks than other currently available large car datasets.

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Data-driven prediction of unsteady flow over a circular cylinder using deep learning

Journal of Fluid Mechanics ◽

10.1017/jfm.2019.700 ◽

2019 ◽

Vol 879 ◽

pp. 217-254 ◽

Cited By ~ 23

Author(s):

Sangseung Lee ◽

Donghyun You

Keyword(s):

Deep Learning ◽

Circular Cylinder ◽

Unsteady Flow ◽

Conservation Laws ◽

Flow Fields ◽

Loss Functions ◽

Generative Adversarial Networks ◽

Learning Networks ◽

Adversarial Networks ◽

Adversarial Training

Unsteady flow fields over a circular cylinder are used for training and then prediction using four different deep learning networks: generative adversarial networks with and without consideration of conservation laws; and convolutional neural networks with and without consideration of conservation laws. Flow fields at future occasions are predicted based on information on flow fields at previous occasions. Predictions of deep learning networks are made for flow fields at Reynolds numbers that were not used during training. Physical loss functions are proposed to explicitly provide information on conservation of mass and momentum to deep learning networks. An adversarial training is applied to extract features of flow dynamics in an unsupervised manner. Effects of the proposed physical loss functions and adversarial training on predicted results are analysed. Captured and missed flow physics from predictions are also analysed. Predicted flow fields using deep learning networks are in good agreement with flow fields computed by numerical simulations.

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Application of Rotating Machinery Fault Diagnosis Based on Deep Learning

Shock and Vibration ◽

10.1155/2021/3083190 ◽

2021 ◽

Vol 2021 ◽

pp. 1-30

Author(s):

Wei Cui ◽

Guoying Meng ◽

Aiming Wang ◽

Xinge Zhang ◽

Jun Ding

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Fault Diagnosis ◽

Rotating Machinery ◽

Normal Operation ◽

Generative Adversarial Networks ◽

Adversarial Networks ◽

Safe Production ◽

Continuous Progress ◽

Machinery Fault Diagnosis

With the continuous progress of modern industry, rotating machinery is gradually developing toward complexity and intelligence. The fault diagnosis technology of rotating machinery is one of the key means to ensure the normal operation of equipment and safe production, which has very important significance. Deep learning is a useful tool for analyzing and processing big data, which has been widely used in various fields. After a brief review of early fault diagnosis methods, this paper focuses on the method models that are widely used in deep learning: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), and transfer learning methods are summarized from the two aspects of principle and application in the field of fault diagnosis of rotating machinery. Then, the commonly used evaluation indicators used to evaluate the performance of rotating machinery fault diagnosis methods are summarized. Finally, according to the current research status in the field of rotating machinery fault diagnosis, the current problems and possible future development and research trends are discussed.

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Object-Oriented Generative Adversarial Networks

10.31219/osf.io/nf7tq ◽

2020 ◽

Author(s):

Qianli Liao

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Object Recognition ◽

Object Oriented ◽

Generative Adversarial Networks ◽

Statistical Features ◽

Connectionist Models ◽

Adversarial Networks ◽

Class Project ◽

High Level

(Performed in 2018 as a class project) Deep learning is a field that has been mainly driven by connectionist models like neural networks, characterized by layered processing of distributed, sub-symbolic and statistical features. However, human high-level thoughts appear to be highly symbolic, focusing on objects and relations.To bridge the gap between perception and symbols, a series of models on "Object Oriented Deep Learning" was proposed [9,8,7]. In this project we further explore this class of models. We implement a generative version of OODL that can generate images instead of performing object recognition, in a similar way to Generative Adversarial Networks (GANs). In comparison to conventional “feature-oriented” deep learning, OODL naturally handles properties of objects by incorporating them as fields. It offers exact equivariance [8] to translation, rotation and scaling. When implementing it as a generative model, one should be able to precisely control such geometric properties of the generated objects.

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Offline Handwritten Signature Authentication with Conditional Deep Convolutional Generative Adversarial Networks

10.5753/eniac.2021.18277 ◽

2021 ◽

Author(s):

David C. Yonekura ◽

Elloá B. Guedes

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Real World ◽

Generative Adversarial Networks ◽

Equal Error Rate ◽

Training Time ◽

Adversarial Networks ◽

Handwritten Signature ◽

State Of Art

Handwritten signature authentication systems are important in many real world scenarios to avoid frauds. Thanks to Deep Learning, state-of-art solutions have been proposed to this problem by making use of Convolutional Neural Networks, but other models in this Machine Learning subarea are still to be further explored. In this perspective, the present article introduces a Conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) approach whose experimental results in a realistic dataset with skilled forgeries have Equal Error Rate (EER) of 18.53% and balanced accuracy of 87.91%. These results validate a writerdependent cDCGAN-based solution to the signature authentication problem in a real world scenario where no forgeries are available nor required in training time.

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Deep CNN and Deep GAN in Computational Visual Perception-Driven Image Analysis

Complexity ◽

10.1155/2021/5541134 ◽

2021 ◽

Vol 2021 ◽

pp. 1-30

Author(s):

R. Nandhini Abirami ◽

P. M. Durai Raj Vincent ◽

Kathiravan Srinivasan ◽

Usman Tariq ◽

Chuan-Yu Chang

Keyword(s):

Neural Networks ◽

Computer Vision ◽

Deep Learning ◽

Visual Perception ◽

Generative Adversarial Networks ◽

Deep Convolutional Neural Networks ◽

Generative Adversarial Network ◽

Biological Vision ◽

Adversarial Network ◽

Adversarial Networks

Computational visual perception, also known as computer vision, is a field of artificial intelligence that enables computers to process digital images and videos in a similar way as biological vision does. It involves methods to be developed to replicate the capabilities of biological vision. The computer vision’s goal is to surpass the capabilities of biological vision in extracting useful information from visual data. The massive data generated today is one of the driving factors for the tremendous growth of computer vision. This survey incorporates an overview of existing applications of deep learning in computational visual perception. The survey explores various deep learning techniques adapted to solve computer vision problems using deep convolutional neural networks and deep generative adversarial networks. The pitfalls of deep learning and their solutions are briefly discussed. The solutions discussed were dropout and augmentation. The results show that there is a significant improvement in the accuracy using dropout and data augmentation. Deep convolutional neural networks’ applications, namely, image classification, localization and detection, document analysis, and speech recognition, are discussed in detail. In-depth analysis of deep generative adversarial network applications, namely, image-to-image translation, image denoising, face aging, and facial attribute editing, is done. The deep generative adversarial network is unsupervised learning, but adding a certain number of labels in practical applications can improve its generating ability. However, it is challenging to acquire many data labels, but a small number of data labels can be acquired. Therefore, combining semisupervised learning and generative adversarial networks is one of the future directions. This article surveys the recent developments in this direction and provides a critical review of the related significant aspects, investigates the current opportunities and future challenges in all the emerging domains, and discusses the current opportunities in many emerging fields such as handwriting recognition, semantic mapping, webcam-based eye trackers, lumen center detection, query-by-string word, intermittently closed and open lakes and lagoons, and landslides.

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Analysis of Application Examples of Differential Privacy in Deep Learning

Computational Intelligence and Neuroscience ◽

10.1155/2021/4244040 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Zhidong Shen ◽

Ting Zhong

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Deep Learning ◽

Deep Neural Networks ◽

Differential Privacy ◽

Generative Adversarial Networks ◽

Learning Models ◽

Privacy Leakage ◽

Adversarial Networks ◽

Inference Attacks

Artificial Intelligence has been widely applied today, and the subsequent privacy leakage problems have also been paid attention to. Attacks such as model inference attacks on deep neural networks can easily extract user information from neural networks. Therefore, it is necessary to protect privacy in deep learning. Differential privacy, as a popular topic in privacy-preserving in recent years, which provides rigorous privacy guarantee, can also be used to preserve privacy in deep learning. Although many articles have proposed different methods to combine differential privacy and deep learning, there are no comprehensive papers to analyze and compare the differences and connections between these technologies. For this purpose, this paper is proposed to compare different differential private methods in deep learning. We comparatively analyze and classify several deep learning models under differential privacy. Meanwhile, we also pay attention to the application of differential privacy in Generative Adversarial Networks (GANs), comparing and analyzing these models. Finally, we summarize the application of differential privacy in deep neural networks.

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Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks

Sensors ◽

10.3390/s21154953 ◽

2021 ◽

Vol 21 (15) ◽

pp. 4953

Author(s):

Sara Al-Emadi ◽

Abdulla Al-Ali ◽

Abdulaziz Al-Ali

Keyword(s):

Neural Network ◽

Deep Learning ◽

Recurrent Neural Network ◽

Learning Algorithms ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Adversarial Networks ◽

Detection And Identification ◽

Learning Techniques ◽

The Impact

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones.

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