AAAI-2021-Paper-List-of-Anomaly-Detection

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AAAI-2021-Paper-List-of-Anomaly-Detection

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A list of accepted papers in AAAI 2021 about anomaly detection.

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# AAAI-2021-Paper-List-of-Anomaly-Detection
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](https://github.com/ZIYU-DEEP/Papers-on-Anomaly-Detection-in-NeurIPS-2020).

Unlike in [NeurIPS'20](https://github.com/ZIYU-DEEP/Papers-on-Anomaly-Detection-in-NeurIPS-2020) 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.*

### 1. New Models for General Anomaly Detection
**GAN  Ensemble  for  Anomaly  Detection** [[arxiv](https://arxiv.org/abs/2012.07988)]  
*Xiaohui  Chen,  Xu  Han,  Liping  Liu*

**Few-Shot  One-Class  Classification  via  Meta‐Learning** [[arxiv](https://arxiv.org/abs/2007.04146)]  
*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](https://arxiv.org/abs/2006.02003)]   
*Alexander Cao, Yuan Luo, Diego Klabjan*

### 2. Improvement on Real-World Anomaly Detection
**Regularizing  Attention  Networks  for  Anomaly  Detection  in  Visual  Question  Answering** [[arxiv](https://arxiv.org/abs/2009.10054)]    
*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](https://bhooi.github.io/papers/gdn_aaai2021.pdf)]       
*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*

### 3. New Functionality of Anomaly Detection
**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*

### 4. Miscellaneous
**Exploratory Machine Learning with Unknown Unknowns** [[arxiv](https://arxiv.org/abs/2002.01605)]     
*Yu-Jie Zhang, Peng Zhao, Zhi-Hua Zhou*

**The Gap on GAP: Tackling the Problem of Differing Data Distributions in Bias-Measuring Datasets** [[arxiv](https://arxiv.org/abs/2011.01837)]    
*Vid Kocijan, Oana-Maria Camburu, Thomas Lukasiewicz*

**DecAug:  Out-of-Distribution  Generalization  via  Decomposed  Feature  Representation  and  Semantic  Augmentation** [[arxiv](https://arxiv.org/abs/2012.09382)]    
*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](https://arxiv.org/abs/2101.03778v1)]  
*Alexander Podolskiy, Dmitry Lipin, Andrey Bout, Ekaterina Artemova, Irina Piontkovskaya*

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