通过充分利用未标记数据进行深度目标检测的主动学习

IF 3.2 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Feixiang Tan, Guansheng Zheng
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引用次数: 0

摘要

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Active learning for deep object detection by fully exploiting unlabeled data
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来源期刊
Connection Science
Connection Science 工程技术-计算机:理论方法
CiteScore
6.50
自引率
39.60%
发文量
94
审稿时长
3 months
期刊介绍: Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.
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