Synthetic Data Generation for Deep Learning of Underwater Disparity Estimation

Olson, Elizabeth A.; Barbalata, Corina; Zhang, Junming; Skinner, Katherine A.; Johnson-Roberson, Matthew

doi:10.1109/oceans.2018.8604489

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…Underwater 2018 [431] Underwater synthetic stereo pairs generator Datasets of basic objects YCB 2015 [467] 77 objects in 5 categories ShapeNet 2015 [93] >3M models, 3135 categories, rich annotations ShapeNetCore 2017 [672] 51K manually verified models from 55 categories…”

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confidence: 99%

“…Olson et al [431] consider an unusual special case for this problem: underwater disparity estimation. Their work is also interesting in the way they produce synthetic data: Olson et al project real underwater images on randomized synthetic surfaces produced in Blender, and then use rendering tools developed to mimic the underwater sensors and characteristic underwater effects such as fast light decay and backscattering.…”

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confidence: 99%

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Synthetic Data for Deep Learning

Nikolenko¹

2019

Preprint

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Synthetic data is an increasingly popular tool for training deep learning models, especially in computer vision but also in other areas. In this work, we attempt to provide a comprehensive survey of the various directions in the development and application of synthetic data. First, we discuss synthetic datasets for basic computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., semantic segmentation), synthetic environments and datasets for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, simulation environments for robotics, applications of synthetic data outside computer vision (in neural programming, bioinformatics, NLP, and more); we also survey the work on improving synthetic data development and alternative ways to produce it such as GANs. Second, we discuss in detail the synthetic-to-real domain adaptation problem that inevitably arises in applications of synthetic data, including syntheticto-real refinement with GAN-based models and domain adaptation at the feature/model level without explicit data transformations. Third, we turn to privacy-related applications of synthetic data and review the work on generating synthetic datasets with differential privacy guarantees. We conclude by highlighting the most promising directions for further work in synthetic data studies.

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confidence: 99%

Section: Name Year Ref Size / Commentsmentioning

confidence: 99%

Synthetic Data for Deep Learning

Nikolenko¹

2019

Preprint

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Synthetic Data for Deep Learning

Nikolenko

2021

Springer Optimization and Its Applications

222

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Aims and ScopeOptimization has continued to expand in all directions at an astonishing rate. New algorithmic and theoretical techniques are continually developing and the diffusion into other disciplines is proceeding at a rapid pace, with a spot light on machine learning, artificial intelligence, and quantum computing. Our knowledge of all aspects of the field has grown even more profound. At the same time, one of the most striking trends in optimization is the constantly increasing emphasis on the interdisciplinary nature of the field. Optimization has been a basic tool in areas not limited to applied mathematics, engineering, medicine, economics, computer science, operations research, and other sciences.The series Springer Optimization and Its Applications (SOIA) aims to publish state-of-the-art expository works (monographs, contributed volumes, textbooks, handbooks) that focus on theory, methods, and applications of optimization. Topics covered include, but are not limited to, nonlinear optimization, combinatorial optimization, continuous optimization, stochastic optimization, Bayesian optimization, optimal control, discrete optimization, multi-objective optimization, and more. New to the series portfolio include Works at the intersection of optimization and machine learning, artificial intelligence, and quantum computing.Volumes from this series are indexed by Web of Science, zbMATH, Mathematical Reviews, and SCOPUS.

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Machine learning based Synthetic Data Generation using Iterative Regression Analysis

Shah

Gandhi

2020

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)

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Synthetic Data Generation for Deep Learning of Underwater Disparity Estimation

Cited by 5 publications

References 28 publications

Synthetic Data for Deep Learning

Synthetic Data for Deep Learning

Synthetic Data for Deep Learning

Machine learning based Synthetic Data Generation using Iterative Regression Analysis

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