標(biāo)題: Titlebook: Synthetic Data for Deep Learning; Sergey I. Nikolenko Book 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license t [打印本頁(yè)] 作者: CLIP 時(shí)間: 2025-3-21 17:37
書(shū)目名稱Synthetic Data for Deep Learning影響因子(影響力)
書(shū)目名稱Synthetic Data for Deep Learning影響因子(影響力)學(xué)科排名
書(shū)目名稱Synthetic Data for Deep Learning網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Synthetic Data for Deep Learning網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Synthetic Data for Deep Learning被引頻次
書(shū)目名稱Synthetic Data for Deep Learning被引頻次學(xué)科排名
書(shū)目名稱Synthetic Data for Deep Learning年度引用
書(shū)目名稱Synthetic Data for Deep Learning年度引用學(xué)科排名
書(shū)目名稱Synthetic Data for Deep Learning讀者反饋
書(shū)目名稱Synthetic Data for Deep Learning讀者反饋學(xué)科排名
作者: 一再遛 時(shí)間: 2025-3-21 21:50 作者: 全部 時(shí)間: 2025-3-22 00:49 作者: Expostulate 時(shí)間: 2025-3-22 06:20 作者: 荒唐 時(shí)間: 2025-3-22 11:40
Sergey I. Nikolenkoganizations, and what happens in them hasbacklash influences on the entire society. Therefore the problem isnot the management of the individual organization, but themacroconception of management, which in the Western world of todayseparates the economic aspects from the social ones, and theindividual organiz978-1-4613-7498-5978-1-4615-5469-1作者: cardiovascular 時(shí)間: 2025-3-22 12:56 作者: 智力高 時(shí)間: 2025-3-22 20:41
Sergey I. Nikolenkoof an arbitrary number of players, while each player can belong to several groups. The third extension of the basic model, studied in section 4.3, considers situations in which communication possibilities are not completely reliable and might sometimes fail. This is represented by means of probabili作者: 砍伐 時(shí)間: 2025-3-22 21:58
Sergey I. Nikolenkoof an arbitrary number of players, while each player can belong to several groups. The third extension of the basic model, studied in section 4.3, considers situations in which communication possibilities are not completely reliable and might sometimes fail. This is represented by means of probabili作者: 粗俗人 時(shí)間: 2025-3-23 05:25
Sergey I. Nikolenkoof an arbitrary number of players, while each player can belong to several groups. The third extension of the basic model, studied in section 4.3, considers situations in which communication possibilities are not completely reliable and might sometimes fail. This is represented by means of probabili作者: Ballad 時(shí)間: 2025-3-23 06:41 作者: ADAGE 時(shí)間: 2025-3-23 10:05
Sergey I. Nikolenko The study is mainly divided into the following aspects: the tremendous importance of accelerating the building of a moderately prosperous society among ethnic minorities and in ethnic minority areas, the overall evaluation, main progress and problems of building a moderately prosperous society in e作者: Ejaculate 時(shí)間: 2025-3-23 15:04 作者: 使殘廢 時(shí)間: 2025-3-23 19:22
Sergey I. Nikolenko The study is mainly divided into the following aspects: the tremendous importance of accelerating the building of a moderately prosperous society among ethnic minorities and in ethnic minority areas, the overall evaluation, main progress and problems of building a moderately prosperous society in e作者: AFFIX 時(shí)間: 2025-3-24 01:16
Sergey I. Nikolenko The study is mainly divided into the following aspects: the tremendous importance of accelerating the building of a moderately prosperous society among ethnic minorities and in ethnic minority areas, the overall evaluation, main progress and problems of building a moderately prosperous society in e作者: Bucket 時(shí)間: 2025-3-24 05:31
Sergey I. Nikolenko The study is mainly divided into the following aspects: the tremendous importance of accelerating the building of a moderately prosperous society among ethnic minorities and in ethnic minority areas, the overall evaluation, main progress and problems of building a moderately prosperous society in e作者: 外面 時(shí)間: 2025-3-24 10:08 作者: 騷動(dòng) 時(shí)間: 2025-3-24 13:29 作者: 波動(dòng) 時(shí)間: 2025-3-24 16:07
Generative Models in Deep Learning,en we will proceed to the main content, generative adversarial networks, discuss various adversarial architectures and loss functions, and give a case study of style transfer with GANs that is directly relevant to synthetic-to-real transfer.作者: 宏偉 時(shí)間: 2025-3-24 22:48 作者: 無(wú)孔 時(shí)間: 2025-3-25 00:22 作者: 別炫耀 時(shí)間: 2025-3-25 05:47
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.作者: BRAWL 時(shí)間: 2025-3-25 07:55 作者: 揮舞 時(shí)間: 2025-3-25 13:51
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.作者: 毛細(xì)血管 時(shí)間: 2025-3-25 18:02
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.作者: SPALL 時(shí)間: 2025-3-25 20:15
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作者: 粗糙濫制 時(shí)間: 2025-3-26 00:25 作者: 字的誤用 時(shí)間: 2025-3-26 06:39
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作者: stressors 時(shí)間: 2025-3-26 09:10
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作者: Spirometry 時(shí)間: 2025-3-26 13:07
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作者: Injunction 時(shí)間: 2025-3-26 18:06
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作者: 影響深遠(yuǎn) 時(shí)間: 2025-3-26 20:59 作者: VEN 時(shí)間: 2025-3-27 02:31
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 作者: 無(wú)底 時(shí)間: 2025-3-27 06:58
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作者: 平息 時(shí)間: 2025-3-27 10:53 作者: Albinism 時(shí)間: 2025-3-27 15:40
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作者: 侵略主義 時(shí)間: 2025-3-27 20:57 作者: 缺陷 時(shí)間: 2025-3-28 00:25 作者: Euphonious 時(shí)間: 2025-3-28 03:47
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.作者: 自愛(ài) 時(shí)間: 2025-3-28 09:26
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?..作者: CHARM 時(shí)間: 2025-3-28 12:42
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.作者: 清晰 時(shí)間: 2025-3-28 16:43 作者: Adrenaline 時(shí)間: 2025-3-28 22:31
Synthetic-to-Real Domain Adaptation and Refinement,to ensure domain adaptation, while the data remains as synthetic as it has been. We will discuss neural architectures for both approaches, including many models based on generative adversarial networks.作者: opprobrious 時(shí)間: 2025-3-28 23:41
Springer Optimization and Its Applicationshttp://image.papertrans.cn/t/image/884355.jpg作者: 失望昨天 時(shí)間: 2025-3-29 05:25 作者: APNEA 時(shí)間: 2025-3-29 09:23
Sergey I. NikolenkoThe first book about synthetic data, an important field which is rapidly rising in popularity throughout machine learning.Provides a wide survey of several different fields where synthetic data is or 作者: Licentious 時(shí)間: 2025-3-29 11:50
978-3-030-75180-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: jet-lag 時(shí)間: 2025-3-29 18:17
Synthetic Data for Deep Learning978-3-030-75178-4Series ISSN 1931-6828 Series E-ISSN 1931-6836 作者: 形上升才刺激 時(shí)間: 2025-3-29 22:29 作者: agonist 時(shí)間: 2025-3-30 01:52 作者: 放肆的你 時(shí)間: 2025-3-30 07:17 作者: 煩人 時(shí)間: 2025-3-30 09:53
Sergey I. Nikolenkomodel, three cornerstones: a set of players, a function that describes the economic possibilities of the players, and communication restrictions between the players. Adjustments to the basic model can consist of other representations of restrictions on communication, of the economic possibilities of作者: 我還要背著他 時(shí)間: 2025-3-30 15:26
Sergey I. Nikolenkomodel, three cornerstones: a set of players, a function that describes the economic possibilities of the players, and communication restrictions between the players. Adjustments to the basic model can consist of other representations of restrictions on communication, of the economic possibilities of
