Multi-Class Anomaly Detection based on Regularized Discriminative Coupled hypersphere-based Feature Adaptation
In anomaly detection, identification of anomalies across diverse product categories is a complex task. This paper introduces a new model by including class discriminative properties obtained by a modified Regularized Discriminative Variational Auto-Encoder (RD-VAE) in the feature extraction process of Coupled-hypersphere-based Feature Adaptation (CFA). By doing so, the proposed Regularized Discriminative Coupled-hypersphere-based Feature Adaptation (RD-CFA), forms a solution for multi-class anomaly detection. By using the discriminative power of RD-VAE to capture intricate class distributions, combined with CFA's robust anomaly detection capability, the proposed method excels in discerning anomalies across various classes. Extensive evaluations on multi-class anomaly detection and localization using the MVTec AD and BeanTech AD datasets showcase the effectiveness of RD-CFA compared to eight leading contemporary methods.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionMulti-class Anomaly DetectionSimilar Papers 제목 키워드 기반
Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning
Anomaly detection in surveillance videos is a challenging task due to the diversity of anomalous video content and duration. In this paper, we consider video anomaly detection as a regression problem with respect to anom…
Anomaly DetectionAnomaly Detection In Surveillance VideosDiversityMultiple Instance Learning+3DDR-ID: Dual Deep Reconstruction Networks Based Image Decomposition for Anomaly Detection
One pivot challenge for image anomaly (AD) detection is to learn discriminative information only from normal class training images. Most image reconstruction based AD methods rely on the discriminative capability of reco…
Adversarial AttackAdversarial Attack DetectionAnomaly DetectionBenchmarking+1Discriminative-Generative Representation Learning for One-Class Anomaly Detection
As a kind of generative self-supervised learning methods, generative adversarial nets have been widely studied in the field of anomaly detection. However, the representation learning ability of the generator is limited s…
Anomaly DetectionRepresentation LearningSelf-Supervised LearningReconstructed Student-Teacher and Discriminative Networks for Anomaly Detection
Anomaly detection is an important problem in computer vision; however, the scarcity of anomalous samples makes this task difficult. Thus, recent anomaly detection methods have used only normal images with no abnormal are…
Anomaly DetectionAnomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach
In this paper, we address an anomaly detection problem in smart power grids using Multimodal Subspace Support Vector Data Description (MS-SVDD). This approach aims to leverage better feature relations by considering the …
Anomaly DetectionEvent DetectionOne-Class Classification