A list of accepted papers in AAAI 2021 about anomaly detection. Relevant NeurIPS'20 papers are summarized at this repository: NIPS 2020 Paper List of Anomaly Detection.
Unlike in NeurIPS'20 where most papers focus on solving an intrinsic bias problem of unsupervised anomaly detection, submissions in AAAI'21 are interested in (1) new learning models on general anomaly detection, or (2) application-specific improvement on real-world anomaly detection models , or (3) new functionality of existing anomaly detection frameworks.
This repository will be updated again once full papers are available on the website of AAAI '21.
GAN Ensemble for Anomaly Detection [arxiv]
Xiaohui Chen, Xu Han, Liping Liu
Few-Shot One-Class Classification via Meta‐Learning [arxiv]
Ahmed Frikha, Denis Krompass, Hans‐Georg Koepken, Volker Tresp
Neighborhood Consensus Networks for Unsupervised Multi-View Outlier Detection
Li Cheng, Yijie Wang, Xinwang Liu
Open-Set Recognition with Gaussian Mixture Variational Autoencoders [arxiv]
Alexander Cao, Yuan Luo, Diego Klabjan
Regularizing Attention Networks for Anomaly Detection in Visual Question Answering [arxiv]
Doyup Lee, Yeongjae Cheon, Wook-Shin Han
Window Loss for Abnormal Finding Classification and Localization in X‐Ray Image with Point-Base Annotation
Xinyu Zhang, Yirui Wang, Chi Tung Cheng, Le Lu, Adam P Harrison, Jing Xiao, ChienHung Liao, Shun Miao
LREN: Low-Rank Embedded Network for Sample-Free Hyperspectral Anomaly Detection
Kai Jiang, Weiying Xie, Jie Lei, Tao Jiang, Yunsong Li
Appearance‐Motion Memory Consistency Network for Video Anomaly Detection
Ruichu Cai, Hao Zhang, Wen Liu, Shenghua Gao, Zhifeng Hao
Graph Neural Network-Based Anomaly Detection in Multivariate Time Series [arxiv]
Ailin Deng, Bryan Hooi
Time Series Anomaly Detection with Multiresolution Ensemble Decoding
Lifeng Shen, Zhongzhong Yu, Qianli Ma, James Tin-Yau Kwok
Graph Neural Network to Dilute Outliers for Refactoring Monolith Application
Utkarsh Desai, Sambaran Bandyopadhyay, Srikanth Tamilselvam
Outlier Impact Characterization for Time Series Data
Jianbo Li, Lecheng Zheng, Yada Zhu, Jingrui He
Learning Semantic Context from Normal Samples for Unsupervised Anomaly Detection
Xudong Yan, Huaidong Zhang, Xuemiao Xu, Xiaowei Hu, Pheng-Ann Heng
Anomaly Attribution with Likelihood Compensation
Tsuyoshi Ide, Amit Dhurandhar, Jiri Navratil, Moninder Singh, Naoki Abe
Exploratory Machine Learning with Unknown Unknowns [arxiv]
Yu-Jie Zhang, Peng Zhao, Zhi-Hua Zhou
The Gap on GAP: Tackling the Problem of Differing Data Distributions in Bias-Measuring Datasets [arxiv]
Vid Kocijan, Oana-Maria Camburu, Thomas Lukasiewicz
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation [arxiv]
Haoyue Bai, Rui Sun, Lanqing Hong, Fengwei Zhou, Nanyang Ye, Han-Jia Ye, Gary Chan, Zhenguo Li
Accelerated Combinatorial Search for Outlier Detection with Provable Bound on Sub-Optimality
Guihong Wan, Haim Schweitzer
Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detection [arxiv]
Alexander Podolskiy, Dmitry Lipin, Andrey Bout, Ekaterina Artemova, Irina Piontkovskaya