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

別炫耀

The Early Days of Synthetic Data,this chapter, we begin with the early days of synthetic data, show some of the earliest models and applications of computer vision, and discuss aspects of computer vision that have always been very hard or even impossible to do without synthetic data.

BRAWL

揮舞

Synthetic Simulated Environments,ts and simulations for outdoor environments (mostly for autonomous driving), indoor environments, and physics-based simulations for robotics. We also make a special case study of datasets for unmanned aerial vehicles and the use of computer games as simulated environments.

毛細(xì)血管

Synthetic Data Outside Computer Vision,is used for fraud and intrusion detection and other applications in the form of network and/or system logs; in Section?., we consider neural programming; Section?. discusses synthetic data generation and use in bioinformatics, and Section?. reviews the (admittedly limited) applications of synthetic data in natural language processing.

SPALL

1931-6828 rvey of several different fields where synthetic data is or .This is the first book on synthetic data for deep learning, and its breadth of coverage may render this book as the default reference on synthetic data for years to come. The book can also serve as an introduction to several other importan

粗糙濫制

字的誤用

Deep Learning and Optimization,ls have been revolutionizing artificial intelligence, significantly advancing state of the art across all fields of machine learning: computer vision, natural language processing, speech and sound processing, generative models, and much more. This book concentrates on synthetic data applications; we

stressors

Deep Neural Networks for Computer Vision,s image classification, object detection, segmentation, 3D scene understanding, object tracking in videos, and many more. Neural approaches to computer vision were originally modeled after the visual cortex of mammals, but soon became a science of their own, with many architectures already developed

Spirometry

Generative Models in Deep Learning, of the target variable conditioned on the input. In this chapter, we consider . models whose purpose is to learn the entire distribution of inputs and be able to sample new inputs from this distribution. We will go through a general introduction to generative models and then proceed to generative m

Injunction

The Early Days of Synthetic Data,istic imagery. But in fact, synthetic data has been used throughout the history of computer vision, starting from its very inception in the 1960s. In this chapter, we begin with the early days of synthetic data, show some of the earliest models and applications of computer vision, and discuss aspect