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Cited by 44Anomaly Detection Techniques and ApplicationsDomain Adaptation and Few-Shot LearningAdversarial Robustness in Machine Learning

SNIPER: Few-shot Learning for Anomaly Detection to Minimize False-negative Rate with Ensured True-positive Rate

Koizumi, Yuma, Murata, Shin, Harada, Noboru, Saito, Shoichiro, Uematsu, Hisashi·2019·ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
DOI 10.1109/icassp.2019.8683667
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Abstract

In anomaly detection systems, overlooking anomalies may result in serious incidents. Thus, when a system overlooks an anomaly, we need to update the system to never overlook the observed type of anomalies twice. There are roughly two possible approaches to solve this problem; re-training the whole system using all training data, or cascading a new specific detector for the overlooked anomaly. The first approach is the most effective solution; however, a huge computational cost and an amount of anomalous training data are required to re-train the system when it consists of a deep-learning-based anomaly detector. We focused on the latter approach and propose a training method for a cascaded specific anomaly detector using few-shot (just 1 to 3) samples. To suppress the false-negative rate of the overlooked anomaly, the proposed method works to decrease the false-positive rate under the constraint of true-positive rate equaling 1. Experimental results show that the proposed method outperformed conventional cross-entropy-based few-shot learning methods.

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Crossref · 10.1109/icassp.2019.8683667

Cites

23 of 23 references verified0 citation errors · 0 not checkable

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  • Chen et al. [59] verified their proposed scheme with a membership inference attack and a technique based on false negative rates (FNRs).

    “Chen et al. [59] verified their proposed scheme with a membership inference attack and a technique based on false negative rates (FNRs) [103], where FNR : FNR = F N TP + F N , TP means that the membership inference attack test samples were considered to be training dataset and FN means the data was deemed to be nontraining data.”

    From Machine Unlearning: A Survey (2024) · cites this paper as [103]

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  • Machine Unlearning: A Survey

    Xu, H., Zhu, T., Zhang, L., Zhou, W., & Yu, P. S. (2024) · ACM Computing Surveys · doi:10.1145/3603620 · Cited by 164 · see citing works

    “[59] veriied their proposed scheme with a membership inference attack and a technique based on false negative rates (FNRs) [103], where FNR: FNR = FN T P+FN , TP means that the membership inference attack test samples were considered to be training dataset and FN means the data was deemed to be non-training data.”
    Method

Related

Authors
Koizumi, Y., Murata, S., Harada, N., Saito, S., & Uematsu, H.
Affiliations
NTT (Japan) (JP)
ORCID
Venue
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publisher
IEEE
Published
2019-05
Volume
2019
Pages
915-919
ISSN
1520-6149 (ISSN-L)
DOI
10.1109/icassp.2019.8683667
OpenAlex
W2938728536
Cited by
44
References
23
Citations by year
2026: 1 · 2025: 4 · 2024: 4 · 2023: 8 · 2022: 9 · 2021: 8 · 2020: 5 · 2019: 5
FWCI
2.58
Type
conference paper
Language
en
Open access
closed
Licence
Locations
  • IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
  • IEEE Conference Proceedings
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
Keywords
anomaly detection, few-shot learning, false negative rate, diagnostic sensitivity, false positive reduction

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