Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation
In this paper, we address the problem of image anomaly detection and segmentation. Anomaly detection involves making a binary decision as to whether an input image contains an anomaly, and anomaly segmentation aims to locate the anomaly on the pixel level. Support vector data description (SVDD) is a long-standing algorithm used for an anomaly detection, and we extend its deep learning variant to the patch-based method using self-supervised learning. This extension enables anomaly segmentation and improves detection performance. As a result, anomaly detection and segmentation performances measured in AUROC on MVTec AD dataset increased by 9.8% and 7.0%, respectively, compared to the previous state-of-the-art methods. Our results indicate the efficacy of the proposed method and its potential for industrial application. Detailed analysis of the proposed method offers insights regarding its behavior, and the code is available online.
Code (3)
Tasks
Anomaly DetectionAnomaly SegmentationSegmentationSelf-Supervised LearningSimilar Papers 제목 키워드 기반
Flow-based SVDD for anomaly detection
We propose FlowSVDD -- a flow-based one-class classifier for anomaly/outliers detection that realizes a well-known SVDD principle using deep learning tools. Contrary to other approaches to deep SVDD, the proposed model i…
Anomaly DetectionOne-class classifierMulti-Class Deep SVDD: Anomaly Detection Approach in Astronomy with Distinct Inlier Categories
With the increasing volume of astronomical data generated by modern survey telescopes, automated pipelines and machine learning techniques have become crucial for analyzing and extracting knowledge from these datasets. A…
Anomaly DetectionAstronomyDASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection
Semi-supervised anomaly detection aims to detect anomalies from normal samples using a model that is trained on normal data. With recent advancements in deep learning, researchers have designed efficient deep anomaly det…
Anomaly DetectionSupervised Anomaly DetectionAnomaly-Injected Deep Support Vector Data Description for Text Outlier Detection
Anomaly detection or outlier detection is a common task in various domains, which has attracted significant research efforts in recent years. Existing works mainly focus on structured data such as numerical or categorica…
Anomaly DetectionOutlier DetectionManifold-regularised Large-Margin $\ell_p$-SVDD for Multidimensional Time Series Anomaly Detection
We generalise the recently introduced large-margin $\ell_p$-SVDD approach to exploit the geometry of data distribution via manifold regularising for time series anomaly detection. Specifically, we formulate a manifold-re…
Time Series Anomaly Detection