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Classifying patterns of known classes and rejecting ambiguous and novel (also called as out-of-distribution (OOD)) inputs are involved in open world pattern recognition.
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Performance evaluation of pattern classifiers for handwritten character recognition
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An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
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Turkergaze: Crowdsourcing saliency with webcam based eye tracking
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
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Scalable person re-identification: A benchmark
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Towards open set deep networks
Abhijit Bendale and Terrance E Boult · 2016
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End to end learning for self-driving cars
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Deep learning
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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A discriminative feature learning approach for deep face recognition
Yandong Wen, Kaipeng Zhang, Zhifeng Li, and Yu Qiao · 2016
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Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun · 2017
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2017
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Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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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
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Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and R Srikant · 2018
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Max-mahalanobis linear discriminant analysis networks
Tianyu Pang, Chao Du, and Jun Zhu · 2018
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Robust classification with convolutional prototype learning
Hong-Ming Yang, Xu-Yao Zhang, Fei Yin, and Cheng-Lin Liu · 2018
Ovanet: One-vs-all network for universal domain adaptation
Kuniaki Saito and Kate Saenko · 2021
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Mohammadreza Salehi, Hossein Mirzaei, Dan Hendrycks, Yixuan Li, Mohammad Hossein Rohban, and Mohammad Sabokrou · 2021
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React: Out-of-distribution detection with rectified activations
Yiyou Sun, Chuan Guo, and Yixuan Li · 2021
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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Generalized out-of-distribution detection: A survey
Jingkang Yang, Kaiyang Zhou, Yixuan Li, and Ziwei Liu · 2021
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Bias-reduced uncertainty estimation for deep neural classifiers
Yonatan Geifman, Guy Uziel, and Ran El-Yaniv · 2019
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Deep anomaly detection with outlier exposure
Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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C2ae: Class conditioned auto-encoder for open-set recognition
Poojan Oza and Vishal M Patel · 2019
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Classification-reconstruction learning for open-set recognition
Ryota Yoshihashi, Wen Shao, Rei Kawakami, Shaodi You, Makoto Iida, and Takeshi Naemura · 2019
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Learning open set network with discriminative reciprocal points
Guangyao Chen, Limeng Qiao, Yemin Shi, Peixi Peng, Jia Li, Tiejun Huang, Shiliang Pu, and Yonghong Tian · 2020
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Towards unknown-aware learning with virtual outlier synthesis
Xuefeng Du, Zhaoning Wang, Mu Cai, and Sharon Li · 2022
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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
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Class-specific semantic reconstruction for open set recognition
Hongzhi Huang, Yu Wang, Qinghua Hu, and Ming-Ming Cheng · 2022
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Poem: Out-of-distribution detection with posterior sampling
Yifei Ming, Ying Fan, and Yixuan Li · 2022
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Out-of-distribution detection with deep nearest neighbors
Yiyou Sun, Yifei Ming, Xiaojin Zhu, and Yixuan Li · 2022
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Calibrated learning to defer with one-vs-all classifiers
Rajeev Verma and Eric Nalisnick · 2022
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Vim: Out-of-distribution with virtual-logit matching
Haoqi Wang, Zhizhong Li, Litong Feng, and Wayne Zhang · 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
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Rethinking confidence calibration for failure prediction
Fei Zhu, Zhen Cheng, Xu-Yao Zhang, and Cheng-Lin Liu · 2022
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Average of pruning: Improving performance and stability of out-of-distribution detection
Zhen Cheng, Fei Zhu, Xu-Yao Zhang, and Cheng-Lin Liu · 2023
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Extremely simple activation shaping for out-of-distribution detection
Andrija Djurisic, Nebojsa Bozanic, Arjun Ashok, and Rosanne Liu · 2023
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A call to reflect on evaluation practices for failure detection in image classification
Paul F Jaeger, Carsten Tim Lüth, Lukas Klein, and Till J. Bungert · 2023
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Neural relation graph: A unified framework for identifying label noise and outlier data
Jang-Hyun Kim, Sangdoo Yun, and Hyun Oh Song · 2023
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Gen: Pushing the limits of softmax-based out-of-distribution detection
Xixi Liu, Yaroslava Lochman, and Christopher Zach · 2023
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A survey on learning to reject
Xu-Yao Zhang, Guo-Sen Xie, Xiuli Li, Tao Mei, and Cheng-Lin Liu · 2023
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Revisiting discriminative vs. generative classifiers: Theory and implications
Chenyu Zheng, Guoqiang Wu, Fan Bao, Yue Cao, Chongxuan Li, and Jun Zhu · 2023
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Openmix: Exploring outlier samples for misclassification detection
Fei Zhu, Zhen Cheng, Xu-Yao Zhang, and Cheng-Lin Liu · 2023
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