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Single shot active learning using pseudo annotators. / Yang, Yazhou; Loog, Marco.

In: Pattern Recognition, Vol. 89, 2019, p. 22-31.

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Yang, Yazhou ; Loog, Marco. / Single shot active learning using pseudo annotators. In: Pattern Recognition. 2019 ; Vol. 89. pp. 22-31.

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@article{4dac5114ddd64c6c999fe3ca2b9d7039,
title = "Single shot active learning using pseudo annotators",
abstract = "Standard active learning assumes that human annotations are always obtainable whenever new samples are selected. This, however, is unrealistic in many real-world applications where human experts are not readily available at all times. In this paper, we consider the single shot setting: all the required samples should be chosen in a single shot and no human annotation can be exploited during the selection process. We propose a new method, Active Learning through Random Labeling (ALRL), which substitutes single human annotator for multiple, what we will refer to as, pseudo annotators. These pseudo annotators always provide uniform and random labels whenever new unlabeled samples are queried. This random labeling enables standard active learning algorithms to also exhibit the exploratory behavior needed for single shot active learning. The exploratory behavior is further enhanced by selecting the most representative sample via minimizing nearest neighbor distance between unlabeled samples and queried samples. Experiments on real-world datasets demonstrate that the proposed method outperforms several state-of-the-art approaches.",
keywords = "Active learning, Exploration and exploitation, Minimizing nearest neighbor distance, Pseudo annotators, Random labeling, Single shot",
author = "Yazhou Yang and Marco Loog",
year = "2019",
doi = "10.1016/j.patcog.2018.12.027",
language = "English",
volume = "89",
pages = "22--31",
journal = "Pattern Recognition",
issn = "0031-3203",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Single shot active learning using pseudo annotators

AU - Yang, Yazhou

AU - Loog, Marco

PY - 2019

Y1 - 2019

N2 - Standard active learning assumes that human annotations are always obtainable whenever new samples are selected. This, however, is unrealistic in many real-world applications where human experts are not readily available at all times. In this paper, we consider the single shot setting: all the required samples should be chosen in a single shot and no human annotation can be exploited during the selection process. We propose a new method, Active Learning through Random Labeling (ALRL), which substitutes single human annotator for multiple, what we will refer to as, pseudo annotators. These pseudo annotators always provide uniform and random labels whenever new unlabeled samples are queried. This random labeling enables standard active learning algorithms to also exhibit the exploratory behavior needed for single shot active learning. The exploratory behavior is further enhanced by selecting the most representative sample via minimizing nearest neighbor distance between unlabeled samples and queried samples. Experiments on real-world datasets demonstrate that the proposed method outperforms several state-of-the-art approaches.

AB - Standard active learning assumes that human annotations are always obtainable whenever new samples are selected. This, however, is unrealistic in many real-world applications where human experts are not readily available at all times. In this paper, we consider the single shot setting: all the required samples should be chosen in a single shot and no human annotation can be exploited during the selection process. We propose a new method, Active Learning through Random Labeling (ALRL), which substitutes single human annotator for multiple, what we will refer to as, pseudo annotators. These pseudo annotators always provide uniform and random labels whenever new unlabeled samples are queried. This random labeling enables standard active learning algorithms to also exhibit the exploratory behavior needed for single shot active learning. The exploratory behavior is further enhanced by selecting the most representative sample via minimizing nearest neighbor distance between unlabeled samples and queried samples. Experiments on real-world datasets demonstrate that the proposed method outperforms several state-of-the-art approaches.

KW - Active learning

KW - Exploration and exploitation

KW - Minimizing nearest neighbor distance

KW - Pseudo annotators

KW - Random labeling

KW - Single shot

UR - http://www.scopus.com/inward/record.url?scp=85059022329&partnerID=8YFLogxK

U2 - 10.1016/j.patcog.2018.12.027

DO - 10.1016/j.patcog.2018.12.027

M3 - Article

VL - 89

SP - 22

EP - 31

JO - Pattern Recognition

T2 - Pattern Recognition

JF - Pattern Recognition

SN - 0031-3203

ER -

ID: 50579630