scholarly journals Synthetic Data Generation for Deep Learning Models

2021 ◽  
Author(s):  
Martin Denk Christoph Petroll
Keyword(s):  
Deep Learning ◽  
Synthetic Data ◽  
Data Generation ◽  
Learning Models ◽  
Synthetic Data Generation

scholarly journals Producing Synthetic Dataset for Human Fall Detection in AR/VR Environments

2021 ◽  
Vol 11 (24) ◽  
pp. 11938
Author(s):  
Denis Zherdev ◽  
Larisa Zherdeva ◽  
Sergey Agapov ◽  
Anton Sapozhnikov ◽  
Artem Nikonorov ◽  
...  
Keyword(s):  
Deep Learning ◽  
Synthetic Data ◽  
Fall Detection ◽  
Human Action ◽  
Human Interaction ◽  
Data Generation ◽  
Synthetic Data Generation ◽  
Training Stage ◽  
Digital Human ◽  
Behaviour Simulation

Human poses and the behaviour estimation for different activities in (virtual reality/augmented reality) VR/AR could have numerous beneficial applications. Human fall monitoring is especially important for elderly people and for non-typical activities with VR/AR applications. There are a lot of different approaches to improving the fidelity of fall monitoring systems through the use of novel sensors and deep learning architectures; however, there is still a lack of detail and diverse datasets for training deep learning fall detectors using monocular images. The issues with synthetic data generation based on digital human simulation were implemented and examined using the Unreal Engine. The proposed pipeline provides automatic “playback” of various scenarios for digital human behaviour simulation, and the result of a proposed modular pipeline for synthetic data generation of digital human interaction with the 3D environments is demonstrated in this paper. We used the generated synthetic data to train the Mask R-CNN-based segmentation of the falling person interaction area. It is shown that, by training the model with simulation data, it is possible to recognize a falling person with an accuracy of 97.6% and classify the type of person’s interaction impact. The proposed approach also allows for covering a variety of scenarios that can have a positive effect at a deep learning training stage in other human action estimation tasks in an VR/AR environment.

scholarly journals SYNTHETIC DATA GENERATION PIPELINE FOR GEOMETRIC DEEP LEARNING IN ARCHITECTURE

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 337-344
Author(s):  
S. Fedorova ◽  
A. Tono ◽  
M. S. Nigam ◽  
J. Zhang ◽  
A. Ahmadnia ◽  
...  
Keyword(s):  
Deep Learning ◽  
Synthetic Data ◽  
Computational Design ◽  
Data Generation ◽  
Learning Tasks ◽  
3D Data ◽  
Synthetic Data Generation ◽  
Building Modeling ◽  
Building Components ◽  
Architectural Knowledge

Abstract. With the growing interest in deep learning algorithms and computational design in the architectural field, the need for large, accessible and diverse architectural datasets increases. Due to the complexity of such 3D datasets, the most widespread techniques of 3D scanning and manual building modeling are very time-consuming, which does not allow to have a sufficiently large open-source dataset. We decided to tackle this problem by constructing a field-specific synthetic data generation pipeline that generates an arbitrary amount of 3D data along with the associated 2D and 3D annotations. The variety of annotations, the flexibility to customize the generated building and dataset parameters make this framework suitable for multiple deep learning tasks, including geometric deep learning that requires direct 3D supervision. Creating our building data generation pipeline we leveraged the experts’ architectural knowledge in order to construct a framework that would be modular, extendable and would provide a sufficient amount of class-balanced data samples. Moreover, we purposefully involve the researcher in the dataset customization allowing the introduction of additional building components, material textures, building classes, number and type of annotations as well as the number of views per 3D model sample. In this way, the framework would satisfy different research requirements and would be adaptable to a large variety of tasks. All code and data is made publicly available: https://cdinstitute.github.io/Building-Dataset-Generator/.

Synthetic Data Generation for Deep Learning of Underwater Disparity Estimation

2018 ◽  
Author(s):  
Elizabeth A. Olson ◽  
Corina Barbalata ◽  
Junming Zhang ◽  
Katherine A. Skinner ◽  
Matthew Johnson-Roberson
Keyword(s):  
Deep Learning ◽  
Synthetic Data ◽  
Disparity Estimation ◽  
Data Generation ◽  
Synthetic Data Generation

Towards synthetic data generation for machine learning models in weather and climate

2020 ◽  
Author(s):  
David Meyer
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Climate Models ◽  
Synthetic Data ◽  
Real Data ◽  
Data Generation ◽  
Learning Models ◽  
Synthetic Data Generation ◽  
Weather And Climate ◽  
Machine Learning Models

<p>The use of real data for training machine learning (ML) models are often a cause of major limitations. For example, real data may be (a) representative of a subset of situations and domains, (b) expensive to produce, (c) limited to specific individuals due to licensing restrictions. Although the use of synthetic data are becoming increasingly popular in computer vision, ML models used in weather and climate models still rely on the use of large real data datasets. Here we present some recent work towards the generation of synthetic data for weather and climate applications and outline some of the major challenges and limitations encountered.</p>

scholarly journals Fake It Till You Make It: Guidelines for Effective Synthetic Data Generation

2021 ◽  
Vol 11 (5) ◽  
pp. 2158
Author(s):  
Fida K. Dankar ◽  
Mahmoud Ibrahim
Keyword(s):  
Machine Learning ◽  
Propensity Score ◽  
Real Life ◽  
Synthetic Data ◽  
Supervised Machine Learning ◽  
Data Generation ◽  
Learning Models ◽  
Healthcare Data ◽  
Synthetic Data Generation ◽  
Machine Learning Models

Synthetic data provides a privacy protecting mechanism for the broad usage and sharing of healthcare data for secondary purposes. It is considered a safe approach for the sharing of sensitive data as it generates an artificial dataset that contains no identifiable information. Synthetic data is increasing in popularity with multiple synthetic data generators developed in the past decade, yet its utility is still a subject of research. This paper is concerned with evaluating the effect of various synthetic data generation and usage settings on the utility of the generated synthetic data and its derived models. Specifically, we investigate (i) the effect of data pre-processing on the utility of the synthetic data generated, (ii) whether tuning should be applied to the synthetic datasets when generating supervised machine learning models, and (iii) whether sharing preliminary machine learning results can improve the synthetic data models. Lastly, (iv) we investigate whether one utility measure (Propensity score) can predict the accuracy of the machine learning models generated from the synthetic data when employed in real life. We use two popular measures of synthetic data utility, propensity score and classification accuracy, to compare the different settings. We adopt a recent mechanism for the calculation of propensity, which looks carefully into the choice of model for the propensity score calculation. Accordingly, this paper takes a new direction with investigating the effect of various data generation and usage settings on the quality of the generated data and its ensuing models. The goal is to inform on the best strategies to follow when generating and using synthetic data.

Synthetic data generation of high-resolution hyperspectral data using DIRSIG

2007 ◽  
Author(s):  
Marek K. Jakubowski ◽  
David Pogorzala ◽  
Timothy J. Hattenberger ◽  
Scott D. Brown ◽  
John R. Schott
Keyword(s):  
High Resolution ◽  
Synthetic Data ◽  
Hyperspectral Data ◽  
Data Generation ◽  
Synthetic Data Generation
Sign in / Sign up

Export Citation Format

Share Document