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Out-of-distribution (OOD) detection is a critical task for reliable predictions over text.
Cross-lingual language model pretraining
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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. 2018 · 2018
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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 · 2019
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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
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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 · 2019
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Simcse: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021 · 2021
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Fine-tuning can distort pretrained features and underperform out-of-distribution
Ananya Kumar, Aditi Raghunathan, Robbie Matthew Jones, Tengyu Ma, and Percy Liang. 2021 · 2021
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kfolden: k-fold ensemble for out-of-distribution detection-fold ensemble for out-of-distribution detection
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Protoinfomax: Prototypical networks with mutual information maximization for out-of-domain detection
Iftitahu Nimah, Meng Fang, Vlado Menkovski, and Mykola Pechenizkiy. 2021 · 2021
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Revisiting mahalanobis distance for transformer-based out-of-domain detection
Alexander Podolskiy, Dmitry Lipin, Andrey Bout, Ekaterina Artemova, and Irina Piontkovskaya. 2021 · 2021
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Pretrained transformers improve out-of-distribution robustness
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Selective question answering under domain shift
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Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. 2020 · 2020
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Enhancing the generalization for intent classification and out-of-domain detection in slu
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Understanding the behaviour of contrastive loss
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Unsupervised out-of-domain detection via pre-trained transformers
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Modeling discriminative representations for out-of-domain detection with supervised contrastive learning
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Out-of-scope intent detection with self-supervision and discriminative training
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Out of distribution detection for medical images
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Towards textual out-of-domain detection without in-domain labels
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Delving into out-of-distribution detection with vision-language representations
Yifei Ming, Ziyang Cai, Jiuxiang Gu, Yiyou Sun, Wei Li, and Yixuan Li. 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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Mitigating neural network overconfidence with logit normalization
Hongxin Wei, Renchunzi Xie, Hao Cheng, Lei Feng, Bo An, and Yixuan Li. 2022 · 2022
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How to exploit hyperspherical embeddings for out-of-distribution detection?
Yifei Ming, Yiyou Sun, Ousmane Dia, and Yixuan Li. 2023 · 2023
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