Titlebook: Synthetic Data for Deep Learning; Sergey I. Nikolenko Book 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license t

影響深遠(yuǎn)

VEN

Synthetic Simulated Environments,hat can be used either to generate synthetic datasets on the fly or provide learning environments for reinforcement learning agents. We discuss datasets and simulations for outdoor environments (mostly for autonomous driving), indoor environments, and physics-based simulations for robotics. We also

無(wú)底

Synthetic Data Outside Computer Vision,ntirely dependent on synthetic data. In this chapter, we survey some of these fields. Specifically, Section?. discusses how structured synthetic data is used for fraud and intrusion detection and other applications in the form of network and/or system logs; in Section?., we consider neural programmi

平息

Albinism

Synthetic-to-Real Domain Adaptation and Refinement,r, we give a survey of domain adaptation approaches that have been used for synthetic-to-real adaptation, that is, methods for making models trained on synthetic data work well on real data, which is almost always the end goal. We distinguish two main approaches. In . input synthetic data is modifie

侵略主義

缺陷

Euphonious

Deep Neural Networks for Computer Vision, and new ones appearing up to this day. In this chapter, we discuss the most popular architectures for computer vision, concentrating mainly on ideas rather than specific models. We also discuss the first step towards synthetic data for computer vision: data augmentation.

自愛(ài)

Directions in Synthetic Data Development,c data from real images by cutting and pasting (Section?.), and finally possibilities to produce synthetic data by generative models (Section?.). The latter means generating useful synthetic data from scratch rather than domain adaptation and refinement, which we consider in a separate Chapter?..

CHARM

Privacy Guarantees in Synthetic Data,in this regard can be provided by the framework of differential privacy. We give a brief introduction to differential privacy, its relation to machine learning, and the guarantees that it can provide for synthetic data generation.