Relative Attributes-based Generative Adversarial Network for Desert Seismic Noise Suppression

AbstractGenerative adversarial network (GAN) has been applied for low-dose CT images to predict normal-dose CT images. However, the undesired artifacts and details bring uncertainty to the clinical diagnosis. In order to improve the visual quality while suppressing the noise, in this paper, we mainly studied the two key components of deep learning based low-dose CT (LDCT) restoration models—network architecture and adversarial loss, and proposed a disentangled noise suppression method based on GAN (DNSGAN) for LDCT. Specifically, a generator network, which contains the noise suppression and structure recovery modules, is proposed. Furthermore, a multi-scaled relativistic adversarial loss is introduced to preserve the finer structures of generated images. Experiments on simulated and real LDCT datasets show that the proposed method can effectively remove noise while recovering finer details and provide better visual perception than other state-of-the-art methods.

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Noise Modeling to Build Training Sets for Robust Speech Enhancement

10.21203/rs.3.rs-105360/v1 ◽

2020 ◽

Author(s):

Yahui Wang ◽

Wenxi Zhang ◽

Yongbiao Wang ◽

Xinxin Kong ◽

Hongxin Zhang

Keyword(s):

Speech Enhancement ◽

Noise Suppression ◽

Short Term Memory ◽

Noise Distribution ◽

Generative Adversarial Network ◽

Noise Modeling ◽

Real Noise ◽

Adversarial Network ◽

Conditional Information ◽

Training Sets

Abstract The performance of Deep Neural Network (DNN)-based speech enhancement models degrades significantly in real recordings because the synthetic training sets are mismatched with real test sets. To solve this problem, we propose a new Generative Adversarial Network framework for Noise Modeling (NM-GAN) that can build training sets by imitating real noise distribution. The framework combines a novel U-Net with two bidirectional Long Short-Term Memory (LSTM) layers that act as a generator to construct complex noise. The Gaussian distribution is adapted and used as conditional information to direct the noise generation. A discriminator then learns to determine whether a noise sample is from the model distribution or from a real noise distribution. By adversarial and alternate training, NM-GAN can generate enough recall (diversity) and precision (quality of noise) in its samples for it to look like real noise. Afterwards, realistic-looking paired training sets are composed. Extensive experiments were carried out and qualitative and quantitative evaluation of the generated noise samples and training sets demonstrate that potential of the framework. An Speech enhancement model trained on our synthetic training sets and on real training sets was found to be capable of good noise suppression for real speech-related noise.

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Depth from stacked light field images using generative adversarial network

Electronic Imaging ◽

10.2352/issn.2470-1173.2019.11.ipas-270 ◽

2019 ◽

Vol 2019 (11) ◽

pp. 270-1-270-8

Author(s):

Ji-Hun Mun ◽

Yo-Sung Ho

Keyword(s):

Light Field ◽

Generative Adversarial Network ◽

Adversarial Network

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Flight Trajectory Pattern Generalization and Abnormal Flight Detection with Generative Adversarial Network

AIAA Scitech 2021 Forum ◽

10.2514/6.2021-0775 ◽

2021 ◽

Author(s):

Muhammet Aksoy ◽

Orhan Ozdemir ◽

Guney Guner ◽

Baris Baspinar ◽

Emre Koyuncu

Keyword(s):

Flight Trajectory ◽

Generative Adversarial Network ◽

Pattern Generalization ◽

Adversarial Network ◽

Trajectory Pattern

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ORGANIC (1).pdf

10.26434/chemrxiv.5309668.v1 ◽

2017 ◽

Author(s):

Benjamin Sanchez-Lengeling ◽

Carlos Outeiral ◽

Gabriel L. Guimaraes ◽

Alan Aspuru-Guzik

Keyword(s):

Machine Learning ◽

Learning Community ◽

Chemical Species ◽

Material Design ◽

Organic Photovoltaic ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

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Completely Unsupervised Phoneme Recognition by a Generative Adversarial Network Harmonized with Iteratively Refined Hidden Markov Models

10.21437/interspeech.2019-2068 ◽

2019 ◽

Author(s):

Kuan-Yu Chen ◽

Che-Ping Tsai ◽

Da-Rong Liu ◽

Hung-Yi Lee ◽

Lin-shan Lee

Keyword(s):

Hidden Markov Models ◽

Markov Models ◽

Hidden Markov ◽

Phoneme Recognition ◽

Generative Adversarial Network ◽

Adversarial Network

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Role of General Adversarial Networks in Mammogram Analysis: A Review

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405614666191115102318 ◽

2020 ◽

Vol 16 (7) ◽

pp. 863-877

Author(s):

Annapoorani Gopal ◽

Lathaselvi Gandhimaruthian ◽

Javid Ali

Keyword(s):

Breast Tumor ◽

Deep Neural Networks ◽

Training Data ◽

Learning Technology ◽

Breast Cancers ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Tumor Extraction

The Deep Neural Networks have gained prominence in the biomedical domain, becoming the most commonly used networks after machine learning technology. Mammograms can be used to detect breast cancers with high precision with the help of Convolutional Neural Network (CNN) which is deep learning technology. An exhaustive labeled data is required to train the CNN from scratch. This can be overcome by deploying Generative Adversarial Network (GAN) which comparatively needs lesser training data during a mammogram screening. In the proposed study, the application of GANs in estimating breast density, high-resolution mammogram synthesis for clustered microcalcification analysis, effective segmentation of breast tumor, analysis of the shape of breast tumor, extraction of features and augmentation of the image during mammogram classification have been extensively reviewed.

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