SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch
- URL: http://arxiv.org/abs/2212.00173v1
- Date: Wed, 30 Nov 2022 23:39:11 GMT
- Title: SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch
- Authors: Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan O. Arik, Tomas
Pfister
- Abstract summary: SPADE shows state-of-the-art semi-supervised anomaly detection performance across a wide range of scenarios with distribution mismatch.
In some common real-world settings such as model facing new types of unlabeled anomalies, SPADE outperforms the state-of-the-art alternatives by 5% AUC in average.
- Score: 58.04518381476167
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Semi-supervised anomaly detection is a common problem, as often the datasets
containing anomalies are partially labeled. We propose a canonical framework:
Semi-supervised Pseudo-labeler Anomaly Detection with Ensembling (SPADE) that
isn't limited by the assumption that labeled and unlabeled data come from the
same distribution. Indeed, the assumption is often violated in many
applications - for example, the labeled data may contain only anomalies unlike
unlabeled data, or unlabeled data may contain different types of anomalies, or
labeled data may contain only 'easy-to-label' samples. SPADE utilizes an
ensemble of one class classifiers as the pseudo-labeler to improve the
robustness of pseudo-labeling with distribution mismatch. Partial matching is
proposed to automatically select the critical hyper-parameters for
pseudo-labeling without validation data, which is crucial with limited labeled
data. SPADE shows state-of-the-art semi-supervised anomaly detection
performance across a wide range of scenarios with distribution mismatch in both
tabular and image domains. In some common real-world settings such as model
facing new types of unlabeled anomalies, SPADE outperforms the state-of-the-art
alternatives by 5% AUC in average.
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