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Out-of-distribution (OOD) detection plays a crucial role in ensuring the safety and reliability of deep neural networks in various applications.
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 · 1907
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Universal text representation from BERT: an empirical study
Xiaofei Ma, Zhiguo Wang, Patrick Ng, Ramesh Nallapati, and Bing Xiang. 2019 · 1910
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Why is the mahalanobis distance effective for anomaly detection?
Ryo Kamoi and Kei Kobayashi. 2020 · 2003
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling. 2014 · 2014
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus. 2014 · 2014
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Mai Nguyen, Jason Yosinski, and Jeff Clune. 2015 · 2015
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Waic, but why? generative ensembles for robust anomaly detection
Hyunsun Choi, Eric Jang, and Alexander A Alemi. 2018 · 2018
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Training confidence-calibrated classifiers for detecting out-of-distribution samples
Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin. 2018a · 2018
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin. 2018b · 2018
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Deep anomaly detection with outlier exposure
Dan Hendrycks, Mantas Mazeika, and Thomas G. Dietterich. 2019 · 2019
Cited alongside, same era.
Do deep generative models know what they don’t know?
Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, and Balaji Lakshminarayanan. 2019 · 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 · 2019
Cited alongside, same era.
How to fine-tune BERT for text classification?
Chi Sun, Xipeng Qiu, Yige Xu, and Xuanjing Huang. 2019 · 2019
Cited alongside, same era.
A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses
Malik Boudiaf, Jérôme Rony, Imtiaz Masud Ziko, Eric Granger, Marco Pedersoli, Pablo Piantanida, and Ismail Ben Ayed. 2020 · 2020
Cited alongside, same era.
Can autonomous vehicles identify, recover from, and adapt to distribution shifts?
React: Out-of-distribution detection with rectified activations
Yiyou Sun, Chuan Guo, and Yixuan Li. 2021 · 2021
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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 Y.S. Lam. 2021 · 2021
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Deep open intent classification with adaptive decision boundary
Hanlei Zhang, Hua Xu, and Ting-En Lin. 2021 · 2021
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Contrastive out-of-distribution detection for pretrained transformers
Wenxuan Zhou, Fangyu Liu, and Muhao Chen. 2021a · 2021
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Contrastive out-of-distribution detection for pretrained transformers
Wenxuan Zhou, Fangyu Liu, and Muhao Chen. 2021b · 2021
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Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, and Yarin Gal. 2020 · 2020
Cited alongside, same era.
Pretrained transformers improve out-of-distribution robustness
Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, Adam Dziedzic, Rishabh Krishnan, and Dawn Song. 2020 · 2020
Cited alongside, same era.
Energy-based out-of-distribution detection
Weitang Liu, Xiaoyun Wang, John D. Owens, and Yixuan Li. 2020 · 2020
Cited alongside, same era.
Trust issues: Uncertainty estimation does not enable reliable ood detection on medical tabular data
Dennis Ulmer, Lotta Meijerink, and Giovanni Cinà. 2020 · 2020
Cited alongside, same era.
Likelihood regret: An out-of-distribution detection score for variational auto-encoder
Zhisheng Xiao, Qing Yan, and Yali Amit. 2020 · 2020
Cited alongside, same era.
Exploring the role of BERT token representations to explain sentence probing results
Hosein Mohebbi, Ali Modarressi, and Mohammad Taher Pilehvar. 2021 · 2021
Cited alongside, same era.
Density of states estimation for out of distribution detection
Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher, Balaji Lakshminarayanan, Alexander A. Alemi, and Joshua V. Dillon. 2021 · 2021
Cited alongside, same era.
Hyunsoo Cho, Choonghyun Park, Jaewook Kang, Kang Min Yoo, Taeuk Kim, and Sang-goo Lee. 2022 · 2022
Later among the works it cites.
Scaling out-of-distribution detection for real-world settings
Dan Hendrycks, Steven Basart, Mantas Mazeika, Andy Zou, Joseph Kwon, Mohammadreza Mostajabi, Jacob Steinhardt, and Dawn Song. 2022 · 2022
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Out-of-distribution detection with deep nearest neighbors
Yiyou Sun, Yifei Ming, Xiaojin Zhu, and Yixuan Li. 2022 · 2022
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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, Xuefeng Du, Kaiyang Zhou, Wayne Zhang, Dan Hendrycks, Yixuan Li, and Ziwei Liu. 2022 · 2022
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The tilted variational autoencoder: Improving out-of-distribution detection
Griffin Floto, Stefan Kremer, and Mihai Nica. 2023 · 2023
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How good are large language models at out-of-distribution detection?
Bo Liu, Liming Zhan, Zexin Lu, Yujie Feng, Lei Xue, and Xiao-Ming Wu. 2023 · 2023
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Is fine-tuning needed? pre-trained language models are near perfect for out-of-domain detection
Rheeya Uppaal, Junjie Hu, and Yixuan Li. 2023 · 2023
Later among the works it cites.