QuGAN: Quasi Generative Adversarial Network for Tibetan Question Answering Corpus Generation

We propose a novel neural network model that aims to generate diverse and human-like natural language questions. Our model not only directly captures the variability in possible questions by using a latent variable, but also generates certain types of questions by introducing an additional observed variable. We deploy our model in the generative adversarial network (GAN) framework and modify the discriminator which not only allows evaluating the question authenticity, but predicts the question type. Our model is trained and evaluated on a question-answering dataset SQuAD, and the experimental results shown the proposed model is able to generate diverse and readable questions with the specific attribute.

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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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