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When first deploying an anomaly detection system, e.g., to detect out-of-scope queries in chatbots, there are no observed data, making data-driven approaches ineffective.
Deep unknown intent detection with margin loss
Ting-En Lin and Hua Xu. 2019 · 1906
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An evaluation dataset for intent classification and out-of-scope prediction
Stefan Larson, Anish Mahendran, Joseph J Peper, Christopher Clarke, Andrew Lee, Parker Hill, Jonathan K Kummerfeld, Kevin Leach, Michael A Laurenzano, Lingjia Tang, et al. 2019 · 1909
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Support vector method for novelty detection
Bernhard Scholkopf, Robert C Williamson, Alex J Smola, John Shawe-Taylor, and John C Platt. 2000 · 2000
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A geometric framework for unsupervised anomaly detection
Eleazar Eskin, Andrew Arnold, Michael Prerau, Leonid Portnoy, and Sal Stolfo. 2002 · 2002
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Efficient intent detection with dual sentence encoders
Iñigo Casanueva, Tadas Temčinas, Daniela Gerz, Matthew Henderson, and Ivan Vulić. 2020 · 2003
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Support vector data description
David MJ Tax and Robert PW Duin. 2004 · 2004
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Outlier detection with kernel density functions
Longin Jan Latecki, Aleksandar Lazarevic, and Dragoljub Pokrajac. 2007 · 2007
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Principal component analysis
Ian Jolliffe. 2011 · 2011
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Ensemble gaussian mixture models for probability density estimation
Michael Glodek, Martin Schels, and Friedhelm Schwenker. 2013 · 2013
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Deep one-class classification
Lukas Ruff, Nico Gornitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Robert Vandermeulen, Alexander Binder, Emmanuel Müller, and Marius Kloft. 2018 · 2018
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Using self-supervised learning can improve model robustness and uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song. 2019 · 2019
Cited alongside, same era.
Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. 2020 · 2020
Cited alongside, same era.
Discovering new intents via constrained deep adaptive clustering with cluster refinement
Ting-En Lin, Hua Xu, and Hanlei Zhang. 2020 · 2020
Cited alongside, same era.
Mpnet: Masked and permuted pre-training for language understanding
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2020 · 2020
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A deep generative distance-based classifier for out-of-domain detection with mahalanobis space
Hong Xu, Keqing He, Yuanmeng Yan, Sihong Liu, Zijun Liu, and Weiran Xu. 2020 · 2020
Cited alongside, same era.
Out-of-scope intent detection with self-supervision and discriminative training
Li-Ming Zhan, Haowen Liang, Bo Liu, Lu Fan, Xiao-Ming Wu, and Albert Lam. 2021 · 2021
Later among the works it cites.
Disentangled knowledge transfer for ood intent discovery with unified contrastive learning
Yutao Mou, Keqing He, Yanan Wu, Zhiyuan Zeng, Hong Xu, Huixing Jiang, Wei Wu, and Weiran Xu. 2022 · 2022
Later among the works it cites.
Anomaly detection requires better representations
Tal Reiss, Niv Cohen, Eliahu Horwitz, Ron Abutbul, and Yedid Hoshen. 2022 · 2022
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Winclip: Zero-/few-shot anomaly classification and segmentation
Jongheon Jeong, Yang Zou, Taewan Kim, Dongqing Zhang, Avinash Ravichandran, and Onkar Dabeer. 2023 · 2023
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Towards general text embeddings with multi-stage contrastive learning
Zehan Li, Xin Zhang, Yanzhao Zhang, Dingkun Long, Pengjun Xie, and Meishan Zhang. 2023 · 2023
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Yinhe Zheng, Guanyi Chen, and Minlie Huang. 2020 · 2020
Cited alongside, same era.
Neural transformation learning for deep anomaly detection beyond images
Chen Qiu, Timo Pfrommer, Marius Kloft, Stephan Mandt, and Maja Rudolph. 2021 · 2021
Cited alongside, same era.
Panda: Adapting pretrained features for anomaly detection and segmentation
Tal Reiss, Niv Cohen, Liron Bergman, and Yedid Hoshen. 2021 · 2021
Cited alongside, same era.
Zhiyuan Zeng, Keqing He, Yuanmeng Yan, Zijun Liu, Yanan Wu, Hong Xu, Huixing Jiang, and Weiran Xu. 2021 · 2021
Cited alongside, same era.
Deep open intent classification with adaptive decision boundary
Hanlei Zhang, Hua Xu, and Ting-En Lin. 2021a
Cited in the paper.
Discovering new intents with deep aligned clustering
Hanlei Zhang, Hua Xu, Ting-En Lin, and Rui Lyu. 2021b
Cited in the paper.
Jianguo Zhang, Kazuma Hashimoto, Yao Wan, Zhiwei Liu, Ye Liu, Caiming Xiong, and Philip S Yu. 2021c
Cited in the paper.
Later among the works it cites.
Mean-shifted contrastive loss for anomaly detection
Tal Reiss and Yedid Hoshen. 2023 · 2023
Later among the works it cites.
Destseg: Segmentation guided denoising student-teacher for anomaly detection
Xuan Zhang, Shiyu Li, Xi Li, Ping Huang, Jiulong Shan, and Ting Chen. 2023 · 2023
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Musc: Zero-shot industrial anomaly classification and segmentation with mutual scoring of the unlabeled images
Xurui Li, Ziming Huang, Feng Xue, and Yu Zhou. 2024 · 2024
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Anomalyclip: Object-agnostic prompt learning for zero-shot anomaly detection
Qihang Zhou, Guansong Pang, Yu Tian, Shibo He, and Jiming Chen. 2024 · 2024
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