"anomaly detection" Papers
31 papers found
ADPretrain: Advancing Industrial Anomaly Detection via Anomaly Representation Pretraining
Xincheng Yao, Yan Luo, Zefeng Qian et al.
CATCH: Channel-Aware Multivariate Time Series Anomaly Detection via Frequency Patching
Xingjian Wu, Xiangfei Qiu, Zhengyu Li et al.
DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detector
Jinghan Li, Yuan Gao, Jinda Lu et al.
Fairness-aware Anomaly Detection via Fair Projection
Feng Xiao, Xiaoying Tang, Jicong Fan
Kaputt: A Large-Scale Dataset for Visual Defect Detection
Sebastian Höfer, Dorian Henning, Artemij Amiranashvili et al.
Multivariate Time Series Anomaly Detection with Idempotent Reconstruction
Xin Sun, Heng Zhou, Chao Li
Odd-One-Out: Anomaly Detection by Comparing with Neighbors
Ankan Kumar Bhunia, Changjian Li, Hakan Bilen
Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts
Chiao-An Yang, Kuan-Chuan Peng, Raymond A. Yeh
A Comprehensive Augmentation Framework for Anomaly Detection
Lin Jiang, Yaping Yan
A Diffusion-Based Framework for Multi-Class Anomaly Detection
Haoyang He, Jiangning Zhang, Hongxu Chen et al.
Beyond Individual Input for Deep Anomaly Detection on Tabular Data
Hugo Thimonier, Fabrice Popineau, Arpad Rimmel et al.
Contamination-Resilient Anomaly Detection via Adversarial Learning on Partially-Observed Normal and Anomalous Data
Wenxi Lv, Qinliang Su, Hai Wan et al.
Continuous Memory Representation for Anomaly Detection
Joo Chan Lee, Taejune Kim, Eunbyung Park et al.
Explain Temporal Black-Box Models via Functional Decomposition
Linxiao Yang, Yunze Tong, Xinyue Gu et al.
FedREDefense: Defending against Model Poisoning Attacks for Federated Learning using Model Update Reconstruction Error
Yueqi Xie, Minghong Fang, Neil Gong
GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features
Luc Sträter, Mohammadreza Salehi, Efstratios Gavves et al.
GOODAT: Towards Test-Time Graph Out-of-Distribution Detection
Luzhi Wang, Di Jin, He Zhang et al.
Learning Anomalies with Normality Prior for Unsupervised Video Anomaly Detection
Haoyue Shi, Le Wang, Sanping Zhou et al.
MF-CLR: Multi-Frequency Contrastive Learning Representation for Time Series
Jufang Duan, Wei Zheng, Yangzhou Du et al.
ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models
Dongha Kim, Jaesung Hwang, Jongjin Lee et al.
Online Adaptive Anomaly Thresholding with Confidence Sequences
Sophia Sun, Abishek Sankararaman, Balakrishnan Narayanaswamy
Online Isolation Forest
Filippo Leveni, Guilherme Weigert Cassales, Bernhard Pfahringer et al.
Root Cause Analysis in Microservice Using Neural Granger Causal Discovery
Cheng-Ming Lin, Ching Chang, Wei-Yao Wang et al.
Single-Model Attribution of Generative Models Through Final-Layer Inversion
Mike Laszkiewicz, Jonas Ricker, Johannes Lederer et al.
Sobolev Space Regularised Pre Density Models
Mark Kozdoba, Binyamin Perets, Shie Mannor
Timer: Generative Pre-trained Transformers Are Large Time Series Models
Yong Liu, Haoran Zhang, Chenyu Li et al.
TimesURL: Self-Supervised Contrastive Learning for Universal Time Series Representation Learning
jiexi Liu, Songcan Chen
TSLANet: Rethinking Transformers for Time Series Representation Learning
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen et al.
UP2ME: Univariate Pre-training to Multivariate Fine-tuning as a General-purpose Framework for Multivariate Time Series Analysis
Yunhao Zhang, Liu Minghao, Shengyang Zhou et al.
VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentation
Zhen Qu, Xian Tao, Mukesh Prasad et al.
When and How Does In-Distribution Label Help Out-of-Distribution Detection?
Xuefeng Du, Yiyou Sun, Sharon Li