Fetching the paper…
Reading the bibliography…
Out-of-distribution (OOD) detection is essential for the reliable and safe deployment of machine learning systems in the real world.
Principles of risk minimization for learning theory
Vladimir Vapnik · 1991
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Foundations of statistical natural language processing
Christopher Manning and Hinrich Schutze · 1999
Earlier work this paper cites.
Lof: identifying density-based local outliers
Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander · 2000
Earlier work this paper cites.
A perspective view and survey of meta-learning
Ricardo Vilalta and Youssef Drissi · 2002
Earlier work this paper cites.
Learning to classify texts using positive and unlabeled data
Xiaoli Li and Bing Liu · 2003
Earlier work this paper cites.
Domain adaptation with structural correspondence learning
John Blitzer, Ryan McDonald, and Fernando Pereira · 2006
Earlier work this paper cites.
The relationship between precision-recall and roc curves
Jesse Davis and Mark Goadrich · 2006
Earlier work this paper cites.
Learning from positive and unlabeled examples: A survey
Bangzuo Zhang and Wanli Zuo · 2008
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Probability models for open set recognition
Walter J Scheirer, Lalit P Jain, and Terrance E Boult · 2014
Earlier work this paper cites.
Survey on anomaly detection using data mining techniques
Shikha Agrawal and Jitendra Agrawal · 2015
Earlier work this paper cites.
Towards open world recognition
Abhijit Bendale and Terrance Boult · 2015
Earlier work this paper cites.
Short text clustering via convolutional neural networks
Jiaming Xu, Peng Wang, Guanhua Tian, Bo Xu, Jun Zhao, Fangyuan Wang, and Hongwei Hao · 2015
Earlier work this paper cites.
Concrete problems in ai safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
Earlier work this paper cites.
Towards open set deep networks
Abhijit Bendale and Terrance E Boult · 2016
Earlier work this paper cites.
Breaking the closed world assumption in text classification
Geli Fei and Bing Liu · 2016
Earlier work this paper cites.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Earlier work this paper cites.
An empirical analysis of formality in online communication
Ellie Pavlick and Joel Tetreault · 2016
Earlier work this paper cites.
Experience report: Log mining using natural language processing and application to anomaly detection
Christophe Bertero, Matthieu Roy, Carla Sauvanaud, and Gilles Trédan · 2017
Earlier work this paper cites.
Enhanced lstm for natural language inference
Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, and Diana Inkpen · 2017
Earlier work this paper cites.
Selective classification for deep neural networks
Yonatan Geifman and Ran El-Yaniv · 2017
Earlier work this paper cites.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2017
Earlier work this paper cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Earlier work this paper cites.
Neural sentence embedding using only in-domain sentences for out-of-domain sentence detection in dialog systems
Seonghan Ryu, Seokhwan Kim, Junhwi Choi, Hwanjo Yu, and Gary Geunbae Lee · 2017
Earlier work this paper cites.
Doc: Deep open classification of text documents
Lei Shu, Hu Xu, and Bing Liu · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Seqgan: Sequence generative adversarial nets with policy gradient
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu · 2017
Earlier work this paper cites.
Recent advances in convolutional neural networks
Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Gang Wang, Jianfei Cai, et al · 2018
Earlier work this paper cites.
Joo-Kyung Kim and Young-Bum Kim · 2018
Earlier work this paper cites.
Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and R Srikant · 2018
Earlier work this paper cites.
Out-of-domain detection based on generative adversarial network
Seonghan Ryu, Sangjun Koo, Hwanjo Yu, and Gary Geunbae Lee · 2018
Earlier work this paper cites.
Out-of-distribution detection using an ensemble of self supervised leave-out classifiers
Apoorv Vyas, Nataraj Jammalamadaka, Xia Zhu, Dipankar Das, Bharat Kaul, and Theodore L Willke · 2018
Earlier work this paper cites.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
Earlier work this paper cites.
Deep autoencoding gaussian mixture model for unsupervised anomaly detection
Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen · 2018
Earlier work this paper cites.
Deep learning for anomaly detection: A survey
Raghavendra Chalapathy and Sanjay Chawla · 2019
Earlier work this paper cites.
Statistical analysis of nearest neighbor methods for anomaly detection
Xiaoyi Gu, Leman Akoglu, and Alessandro Rinaldo · 2019
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
Earlier work this paper cites.
Calibration of encoder decoder models for neural machine translation
Aviral Kumar and Sunita Sarawagi · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Deep unknown intent detection with margin loss
Ting-En Lin and Hua Xu · 2019
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
Cited alongside, same era.
A framework for anomaly detection using language modeling, and its applications to finance
Armineh Nourbakhsh and Grace Bang · 2019
Cited alongside, same era.
Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
Cited alongside, same era.
Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller · 2019
Cited alongside, same era.
Likelihood ratios for out-of-distribution detection
Jie Ren, Peter J Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark Depristo, Joshua Dillon, and Balaji Lakshminarayanan · 2019
Revisiting mahalanobis distance for transformer-based out-of-domain detection
Alexander Podolskiy, Dmitry Lipin, Andrey Bout, Ekaterina Artemova, and Irina Piontkovskaya · 2021
Later among the works it cites.
Pnpood: Out-of-distribution detection for text classification via plug andplay data augmentation
Mrinal Rawat, Ramya Hebbalaguppe, and Lovekesh Vig · 2021
Later among the works it cites.
Enhancing the generalization for intent classification and out-of-domain detection in slu
Yilin Shen, Yen-Chang Hsu, Avik Ray, and Hongxia Jin · 2021
Later among the works it cites.
Odist: Open world classification via distributionally shifted instances
Lei Shu, Yassine Benajiba, Saab Mansour, and Yi Zhang · 2021
Later among the works it cites.
The art of abstention: Selective prediction and error regularization for natural language processing
Ji Xin, Raphael Tang, Yaoliang Yu, and Jimmy Lin · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Transfer learning in natural language processing
Sebastian Ruder, Matthew E. Peters, Swabha Swayamdipta, and Thomas Wolf · 2019
Cited alongside, same era.
Cross-lingual transfer learning for multilingual task oriented dialog
Sebastian Schuster, Sonal Gupta, Rushin Shah, and Mike Lewis · 2019
Cited alongside, same era.
Out-of-domain detection for low-resource text classification tasks
Ming Tan, Yang Yu, Haoyu Wang, Dakuo Wang, Saloni Potdar, Shiyu Chang, and Mo Yu · 2019
Cited alongside, same era.
Manifold mixup: Better representations by interpolating hidden states
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, and Yoshua Bengio · 2019
Cited alongside, same era.
A survey of zero-shot learning: Settings, methods, and applications
Wei Wang, Vincent W Zheng, Han Yu, and Chunyan Miao · 2019
Cited alongside, same era.
Open-world learning and application to product classification
Hu Xu, Bing Liu, Lei Shu, and P Yu · 2019
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
Unsupervised out-of-domain detection via pre-trained transformers
Keyang Xu, Tongzheng Ren, Shikun Zhang, Yihao Feng, and Caiming Xiong · 2021
Later among the works it cites.
Generalized out-of-distribution detection: A survey
Jingkang Yang, Kaiyang Zhou, Yixuan Li, and Ziwei Liu · 2021
Later among the works it cites.
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 YS Lam · 2021
Later among the works it cites.
Deep open intent classification with adaptive decision boundary
Hanlei Zhang, Hua Xu, and Ting-En Lin · 2021
Later among the works it cites.
Holistic sentence embeddings for better out-of-distribution detection
Sishuo Chen, Xiaohan Bi, Rundong Gao, and Xu Sun · 2022
Later among the works it cites.
Enhancing out-of-distribution detection in natural language understanding via implicit layer ensemble
Hyunsoo Cho, Choonghyun Park, Jaewook Kang, Kang Min Yoo, Taeuk Kim, and Sang-goo Lee · 2022
Later among the works it cites.
Barle: Background-aware representation learning for background shift out-of-distribution detection
Hanyu Duan, Yi Yang, Ahmed Abbasi, and Kar Yan Tam · 2022
Later among the works it cites.
Is out-of-distribution detection learnable?
Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, and Feng Liu · 2022
Later among the works it cites.
Out-of-distribution detection in unsupervised continual learning
Jiangpeng He and Fengqing Zhu · 2022
Later among the works it cites.
Continual learning based on ood detection and task masking
Gyuhak Kim, Sepideh Esmaeilpour, Changnan Xiao, and Bing Liu · 2022
Later among the works it cites.
Internet-augmented dialogue generation
Mojtaba Komeili, Kurt Shuster, and Jason Weston · 2022
Later among the works it cites.
Estimating soft labels for out-of-domain intent detection
Hao Lang, Yinhe Zheng, Jian Sun, Fei Huang, Luo Si, and Yongbin Li · 2022
Later among the works it cites.
Delving into out-of-distribution detection with vision-language representations
Yifei Ming, Ziyang Cai, Jiuxiang Gu, Yiyou Sun, Wei Li, and Yixuan Li · 2022
Later among the works it cites.
Provable guarantees for understanding out-of-distribution detection
Peyman Morteza and Yixuan Li · 2022
Later among the works it cites.
Uninl: Aligning representation learning with scoring function for ood detection via unified neighborhood learning
Yutao Mou, Pei Wang, Keqing He, Yanan Wu, Jingang Wang, Wei Wu, and Weiran Xu · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Later among the works it cites.
Out-of-distribution detection with deep nearest neighbors
Yiyou Sun, Yifei Ming, Xiaojin Zhu, and Yixuan Li · 2022
Later among the works it cites.
Investigating selective prediction approaches across several tasks in IID, OOD, and adversarial settings
Neeraj Varshney, Swaroop Mishra, and Chitta Baral · 2022
Later among the works it cites.
Generalizing to unseen domains: A survey on domain generalization
Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, and Philip Yu · 2022
Later among the works it cites.
Revisit overconfidence for OOD detection: Reassigned contrastive learning with adaptive class-dependent threshold
Yanan Wu, Keqing He, Yuanmeng Yan, QiXiang Gao, Zhiyuan Zeng, Fujia Zheng, Lulu Zhao, Huixing Jiang, Wei Wu, and Weiran Xu · 2022
Later among the works it cites.
Openood: Benchmarking generalized out-of-distribution detection
Jingkang Yang, Pengyun Wang, Dejian Zou, Zitang Zhou, Kunyuan Ding, WENXUAN PENG, Haoqi Wang, Guangyao Chen, Bo Li, Yiyou Sun, et al · 2022
Later among the works it cites.
Feed two birds with one scone: Exploiting wild data for both out-of-distribution generalization and detection
Haoyue Bai, Gregory Canal, Xuefeng Du, Jeongyeol Kwon, Robert D Nowak, and Yixuan Li · 2023
Closest in time.
Dream the impossible: Outlier imagination with diffusion models
Xuefeng Du, Yiyou Sun, Xiaojin Zhu, and Yixuan Li · 2023
Closest in time.
Pseudo outlier exposure for out-of-distribution detection using pretrained transformers
Jaeyoung Kim, Kyuheon Jung, Dongbin Na, Sion Jang, Eunbin Park, and Sungchul Choi · 2023
Closest in time.
Out-of-domain intent detection considering multi-turn dialogue contexts
Hao Lang, Yinhe Zheng, Binyuan Hui, Fei Huang, and Yongbin Li · 2023
Closest in time.
Visual classification via description from large language models
Sachit Menon and Carl Vondrick · 2023
Closest in time.
How does fine-tuning impact out-of-distribution detection for vision-language models?
Yifei Ming and Yixuan Li · 2023
Closest in time.
On prefix-tuning for lightweight out-of-distribution detection
Yawen Ouyang, Yongchang Cao, Yuan Gao, Zhen Wu, Jianbing Zhang, and Xinyu Dai · 2023
Closest in time.
Out-of-distribution detection and selective generation for conditional language models
Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna, Mohammad Saleh, Balaji Lakshminarayanan, and Peter J Liu · 2023
Closest in time.
Prompting gpt-3 to be reliable
Chenglei Si, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang, Jordan Boyd-Graber, and Lijuan Wang · 2023
Closest in time.
Non-parametric outlier synthesis
Leitian Tao, Xuefeng Du, Xiaojin Zhu, and Yixuan Li · 2023
Closest in time.
Is fine-tuning needed? pre-trained language models are near perfect for out-of-domain detection
Rheeya Uppaal, Junjie Hu, and Yixuan Li · 2023
Closest in time.
Efficient out-of-domain detection for sequence to sequence models
Artem Vazhentsev, Akim Tsvigun, Roman Vashurin, Sergey Petrakov, Daniil Vasilev, Maxim Panov, Alexander Panchenko, and Artem Shelmanov · 2023
Closest in time.
Multi-level knowledge distillation for out-of-distribution detection in text
Qianhui Wu, Huiqiang Jiang, Haonan Yin, Börje F Karlsson, and Chin-Yew Lin · 2023
Closest in time.
Two birds one stone: Dynamic ensemble for ood intent classification
Yunhua Zhou, Jianqiang Yang, Pengyu Wang, and Xipeng Qiu · 2023
Closest in time.