Synthetic Data for Deep Learning

09/25/2019
by   Sergey I. Nikolenko, et al.
71

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 synthetic-to-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.

READ FULL TEXT

page 14

page 19

page 28

page 30

page 31

page 34

page 36

page 41

05/26/2021

KLIEP-based Density Ratio Estimation for Semantically Consistent Synthetic to Real Images Adaptation in Urban Traffic Scenes

Synthetic data has been applied in many deep learning based computer vis...
05/18/2021

Content Disentanglement for Semantically Consistent Synthetic-to-RealDomain Adaptation in Urban Traffic Scenes

Synthetic data generation is an appealing approach to generate novel tra...
11/09/2020

EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN Scenes

Multimodal large-scale datasets for outdoor scenes are mostly designed f...
07/16/2019

How much real data do we actually need: Analyzing object detection performance using synthetic and real data

In recent years, deep learning models have resulted in a huge amount of ...
10/17/2017

Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications

We present an overview and evaluation of a new, systematic approach for ...
12/11/2017

Domain Adaptation Using Adversarial Learning for Autonomous Navigation

Autonomous navigation has become an increasingly popular machine learnin...
05/28/2021

NViSII: A Scriptable Tool for Photorealistic Image Generation

We present a Python-based renderer built on NVIDIA's OptiX ray tracing e...