Automatic Inference of Demographic Parameters Using Generative Adversarial Networks

AbstractPopulation genetics relies heavily on simulated data for validation, inference, and intuition. In particular, since real data is always limited, simulated data is crucial for training machine learning methods. Simulation software can accurately model evolutionary processes, but requires many hand-selected input parameters. As a result, simulated data often fails to mirror the properties of real genetic data, which limits the scope of methods that rely on it. In this work, we develop a novel approach to estimating parameters in population genetic models that automatically adapts to data from any population. Our method is based on a generative adversarial network that gradually learns to generate realistic synthetic data. We demonstrate that our method is able to recover input parameters in a simulated isolation-with-migration model. We then apply our method to human data from the 1000 Genomes Project, and show that we can accurately recapitulate the features of real data.

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SELF-COLLISION AVOIDANCE OF ARM ROBOT USING GENERATIVE ADVERSARIAL NETWORK AND PARTICLES SWARM OPTIMIZATION (GAN-PSO)

SINERGI ◽

10.22441/sinergi.2021.2.005 ◽

2021 ◽

Vol 25 (2) ◽

pp. 141

Author(s):

Zendi Iklima ◽

Andi Adriansyah ◽

Sabin Hitimana

Keyword(s):

Collision Avoidance ◽

Data Distribution ◽

Real Data ◽

Inverse Kinematic ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Swarm Optimization ◽

Motion Data ◽

Adversarial Network ◽

Body Self

Collision avoidance of Arm Robot is designed for the robot to collide objects, colliding environment, and colliding its body. Self-collision avoidance was successfully trained using Generative Adversarial Networks (GANs) and Particle Swarm Optimization (PSO). The Inverse Kinematics (IK) with 96K motion data was extracted as the dataset to train data distribution of 3.6K samples and 7.2K samples. The proposed method GANs-PSO can solve the common GAN problem such as Mode Collapse or Helvetica Scenario that occurs when the generator always gets the same output point which mapped to different input values. The discriminator produces the random samples' data distribution in which present the real data distribution (generated by Inverse Kinematic analysis). The PSO was successfully reduced the number of training epochs of the generator only with 5000 iterations. The result of our proposed method (GANs-PSO) with 50 particles was 5000 training epochs executed in 0.028ms per single prediction and 0.027474% Generator Mean Square Error (GMSE).

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R2D2-GAN: Unlimited Resolution Image Generation for Acoustic Data

Marine Technology Society Journal ◽

10.4031/mtsj.55.4.11 ◽

2021 ◽

Vol 55 (4) ◽

pp. 99-107

Author(s):

Marija Jegorova ◽

Antti Ilari Karjalainen ◽

Jose Vazquez ◽

Timothy Hospedales

Keyword(s):

Synthetic Data ◽

Real Data ◽

Visual Assessment ◽

Generative Adversarial Network ◽

Acoustic Data ◽

Assessment Tests ◽

Adversarial Network ◽

Seabed Topography ◽

Recognition Systems ◽

Simulated Images

Abstract In this paper, we present a novel simulation technique for generating high-quality images of any predefined resolution. This method can be used to synthesize sonar scans of size equivalent to those collected during a full-length mission, with across-track resolutions of any chosen magnitude. In essence, our model extends generative adversarial network (GAN)-based architecture into a conditional recursive setting that facilitates the continuity of the generated images. The data produced are continuous and realistically looking and can also be generated at least two times faster than the real speed of acquisition for the sonars with higher resolutions, such as EdgeTech. The seabed topography can be fully controlled by the user. The visual assessment tests demonstrate that humans cannot distinguish the simulated images from real ones. Moreover, experimental results suggest that, in the absence of real data, the autonomous recognition systems can benefit greatly from training with the synthetic data, produced by the double-recursive double-discriminator GANs (R2D2-GANs).

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Generation of Synthetic Data with Conditional Generative Adversarial Networks

Logic Journal of IGPL ◽

10.1093/jigpal/jzaa059 ◽

2020 ◽

Author(s):

Belén Vega-Márquez ◽

Cristina Rubio-Escudero ◽

Isabel Nepomuceno-Chamorro

Keyword(s):

Research Work ◽

Synthetic Data ◽

Original Data ◽

Classification Problem ◽

Generative Adversarial Networks ◽

Data Generation ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Original Dataset

Abstract The generation of synthetic data is becoming a fundamental task in the daily life of any organization due to the new protection data laws that are emerging. Because of the rise in the use of Artificial Intelligence, one of the most recent proposals to address this problem is the use of Generative Adversarial Networks (GANs). These types of networks have demonstrated a great capacity to create synthetic data with very good performance. The goal of synthetic data generation is to create data that will perform similarly to the original dataset for many analysis tasks, such as classification. The problem of GANs is that in a classification problem, GANs do not take class labels into account when generating new data, it is treated as any other attribute. This research work has focused on the creation of new synthetic data from datasets with different characteristics with a Conditional Generative Adversarial Network (CGAN). CGANs are an extension of GANs where the class label is taken into account when the new data is generated. The performance of our results has been measured in two different ways: firstly, by comparing the results obtained with classification algorithms, both in the original datasets and in the data generated; secondly, by checking that the correlation between the original data and those generated is minimal.

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Synthesizing electronic health records using improved generative adversarial networks

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocy142 ◽

2018 ◽

Vol 26 (3) ◽

pp. 228-241 ◽

Cited By ~ 15

Author(s):

Mrinal Kanti Baowaly ◽

Chia-Ching Lin ◽

Chao-Lin Liu ◽

Kuan-Ta Chen

Keyword(s):

Electronic Health Records ◽

Binary Data ◽

Synthetic Data ◽

Generative Adversarial Networks ◽

Research Database ◽

Data Generation ◽

Generative Adversarial Network ◽

Health Records ◽

Adversarial Network ◽

Electronic Health

AbstractObjectiveThe aim of this study was to generate synthetic electronic health records (EHRs). The generated EHR data will be more realistic than those generated using the existing medical Generative Adversarial Network (medGAN) method.Materials and MethodsWe modified medGAN to obtain two synthetic data generation models—designated as medical Wasserstein GAN with gradient penalty (medWGAN) and medical boundary-seeking GAN (medBGAN)—and compared the results obtained using the three models. We used 2 databases: MIMIC-III and National Health Insurance Research Database (NHIRD), Taiwan. First, we trained the models and generated synthetic EHRs by using these three 3 models. We then analyzed and compared the models’ performance by using a few statistical methods (Kolmogorov–Smirnov test, dimension-wise probability for binary data, and dimension-wise average count for count data) and 2 machine learning tasks (association rule mining and prediction).ResultsWe conducted a comprehensive analysis and found our models were adequately efficient for generating synthetic EHR data. The proposed models outperformed medGAN in all cases, and among the 3 models, boundary-seeking GAN (medBGAN) performed the best.DiscussionTo generate realistic synthetic EHR data, the proposed models will be effective in the medical industry and related research from the viewpoint of providing better services. Moreover, they will eliminate barriers including limited access to EHR data and thus accelerate research on medical informatics.ConclusionThe proposed models can adequately learn the data distribution of real EHRs and efficiently generate realistic synthetic EHRs. The results show the superiority of our models over the existing model.

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GENERATIVE ADVERSARIAL NETWORKS AS A NOVEL APPROACH FOR TECTONIC FAULT AND FRACTURE EXTRACTION IN HIGH-RESOLUTION SATELLITE AND AIRBORNE OPTICAL IMAGES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2020-1219-2020 ◽

2020 ◽

Vol XLIII-B3-2020 ◽

pp. 1219-1227

Author(s):

B. Jafrasteh ◽

I. Manighetti ◽

J. Zerubia

Keyword(s):

Ground Truth ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Convolutional Networks ◽

Adversarial Network ◽

Adversarial Networks ◽

Optical Images ◽

Novel Approach ◽

Ground Truth Image ◽

Novel Method

Abstract. We develop a novel method based on Deep Convolutional Networks (DCN) to automate the identification and mapping of fracture and fault traces in optical images. The method employs two DCNs in a two players game: a first network, called Generator, learns to segment images to make them resembling the ground truth; a second network, called Discriminator, measures the differences between the ground truth image and each segmented image and sends its score feedback to the Generator; based on these scores, the Generator improves its segmentation progressively. As we condition both networks to the ground truth images, the method is called Conditional Generative Adversarial Network (CGAN). We propose a new loss function for both the Generator and the Discriminator networks, to improve their accuracy. Using two criteria and a manually annotated optical image, we compare the generalization performance of the proposed method to that of a classical DCN architecture, U-net. The comparison demonstrates the suitability of the proposed CGAN architecture. Further work is however needed to improve its efficiency.

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Road images augmentation with synthetic traffic signs using neural networks

Computer Optics ◽

10.18287/2412-6179-co-859 ◽

2021 ◽

Vol 5 (45) ◽

pp. 736-748

Author(s):

A.S. Konushin ◽

B.V. Faizov ◽

V.I. Shakhuro

Keyword(s):

Synthetic Data ◽

Real Data ◽

Training Data ◽

Traffic Sign ◽

Generative Adversarial Network ◽

Traffic Signs ◽

Adversarial Network ◽

Sign Recognition ◽

Variational Autoencoder ◽

Sign Detection

Traffic sign recognition is a well-researched problem in computer vision. However, the state of the art methods works only for frequent sign classes, which are well represented in training datasets. We consider the task of rare traffic sign detection and classification. We aim to solve that problem by using synthetic training data. Such training data is obtained by embedding synthetic images of signs in the real photos. We propose three methods for making synthetic signs consistent with a scene in appearance. These methods are based on modern generative adversarial network (GAN) architectures. Our proposed methods allow realistic embedding of rare traffic sign classes that are absent in the training set. We adapt a variational autoencoder for sampling plausible locations of new traffic signs in images. We demonstrate that using a mixture of our synthetic data with real data improves the accuracy of both classifier and detector.

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Reconstruction of Irregular Missing Seismic Data Using Conditional Generative Adversarial Networks

Geophysics ◽

10.1190/geo2020-0644.1 ◽

2021 ◽

pp. 1-154

Author(s):

Qing Wei ◽

xiangyang Li ◽

Mingpeng Song

Keyword(s):

Missing Data ◽

Gaussian Noise ◽

Seismic Data ◽

Interpolation Method ◽

Synthetic Data ◽

Training Dataset ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Synthetic Datasets

During acquisition, due to economic and natural reasons, irregular missing seismic data are always observed. To improve accuracy in subsequent processing, the missing data should be interpolated. A conditional generative adversarial network (cGAN) consisting of two networks, a generator and a discriminator, is a deep learning model that can be used to interpolate the missing data. However, because cGAN is typically dataset-oriented, the trained network is unable to interpolate a dataset from an area different from that of the training dataset. We design a cGAN based on Pix2Pix GAN to interpolate irregular missing seismic data. A synthetic dataset synthesized from two models is used to train the network. Further, we add a Gaussian-noise layer in the discriminator to fix a vanishing gradient, allowing us to train a more powerful generator. Two synthetic datasets synthesized by two new geological models and two field datasets are used to test the trained cGAN. The test results and the calculated recovered signal-to-noise ratios indicate that although the cGAN is trained using synthetic data, the network can reconstruct irregular missing field seismic data with high accuracy using the Gaussian-noise layer. We test the performances of cGANs trained with different patch sizes in the discriminator to determine the best structure, and we train the networks using different training datasets for different missing rates, demonstrating the best training dataset. Compared with conventional methods, the cGAN based interpolation method does not need different parameter selections for different datasets to obtain the best interpolation data. Furthermore, it is also an efficient technique as the cost is because of the training, and after training, the processing time is negligible.

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MB-GAN: Microbiome Simulation via Generative Adversarial Network

10.1101/863977 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ruichen Rong ◽

Shuang Jiang ◽

Lin Xu ◽

Guanghua Xiao ◽

Yang Xie ◽

...

Keyword(s):

Learning Community ◽

Association Studies ◽

Complex Structure ◽

Simulated Data ◽

Real Data ◽

Original Data ◽

Generative Adversarial Network ◽

Methodology Development ◽

Adversarial Network ◽

Microbiome Data

AbstractSimulation is a critical component of experimental design and evaluation of analysis methods in microbiome association studies. However, statistically modeling the microbiome data is challenging since that the complex structure in the real data is difficult to be fully represented by statistical models. To address this challenge, we designed a novel simulation framework for microbiome data using a generative adversarial network (GAN), called MB-GAN, by utilizing methodology advancements from the deep learning community. MB-GAN can automatically learn from a given dataset and compute simulated datasets that are indistinguishable from it. When MB-GAN was applied to a case-control microbiome study of 396 samples, we demonstrated that the simulated data and the original data had similar first-order and second-order properties, including sparsity, diversities, and taxa-taxa correlations. These advantages are suitable for further microbiome methodology development where high fidelity microbiome data are needed.

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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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Restoring Raindrops Using Attentive Generative Adversarial Networks

Applied Sciences ◽

10.3390/app11157034 ◽

2021 ◽

Vol 11 (15) ◽

pp. 7034

Author(s):

Hee-Deok Yang

Keyword(s):

Weather Conditions ◽

Recurrent Network ◽

Generative Adversarial Networks ◽

Navigation Systems ◽

Vision Systems ◽

Generative Adversarial Network ◽

Network Layers ◽

Adversarial Network ◽

Adversarial Networks ◽

Outdoor Vision

Artificial intelligence technologies and vision systems are used in various devices, such as automotive navigation systems, object-tracking systems, and intelligent closed-circuit televisions. In particular, outdoor vision systems have been applied across numerous fields of analysis. Despite their widespread use, current systems work well under good weather conditions. They cannot account for inclement conditions, such as rain, fog, mist, and snow. Images captured under inclement conditions degrade the performance of vision systems. Vision systems need to detect, recognize, and remove noise because of rain, snow, and mist to boost the performance of the algorithms employed in image processing. Several studies have targeted the removal of noise resulting from inclement conditions. We focused on eliminating the effects of raindrops on images captured with outdoor vision systems in which the camera was exposed to rain. An attentive generative adversarial network (ATTGAN) was used to remove raindrops from the images. This network was composed of two parts: an attentive-recurrent network and a contextual autoencoder. The ATTGAN generated an attention map to detect rain droplets. A de-rained image was generated by increasing the number of attentive-recurrent network layers. We increased the number of visual attentive-recurrent network layers in order to prevent gradient sparsity so that the entire generation was more stable against the network without preventing the network from converging. The experimental results confirmed that the extended ATTGAN could effectively remove various types of raindrops from images.

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