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Out-of-distribution (OOD) detection has received much attention lately due to its importance in the safe deployment of neural networks.
Continual universal object detection
Xialei Liu, Hao Yang, Avinash Ravichandran, Rahul Bhotika, and Stefano Soatto · 2002
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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The pascal visual object classes (VOC) challenge
Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John M. Winn, and Andrew Zisserman · 2010
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Turkergaze: Crowdsourcing saliency with webcam based eye tracking
Pingmei Xu, Krista A Ehinger, Yinda Zhang, Adam Finkelstein, Sanjeev R Kulkarni, and Jianxiong Xiao · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 2015
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Towards open set deep networks
Abhijit Bendale and Terrance E Boult · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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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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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Places: A 10 million image database for scene recognition
Bolei Zhou, Àgata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Detectron
Ross Girshick, Ilija Radosavovic, Georgia Gkioxari, Piotr Dollár, and Kaiming He · 2018
Cited alongside, same era.
Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and Rayadurgam Srikant · 2018
Cited alongside, same era.
Dropout sampling for robust object detection in open-set conditions
Dimity Miller, Lachlan Nicholson, Feras Dayoub, and Niko Sünderhauf · 2018
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz · 2018
Csi: Novelty detection via contrastive learning on distributionally shifted instances
Jihoon Tack, Sangwoo Mo, Jongheon Jeong, and Jinwoo Shin · 2020
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BDD100K: A diverse driving dataset for heterogeneous multitask learning
Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, and Trevor Darrell · 2020
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Triggering failures: Out-of-distribution detection by learning from local adversarial attacks in semantic segmentation
Victor Besnier, Andrei Bursuc, David Picard, and Alexandre Briot · 2021
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The fishyscapes benchmark: Measuring blind spots in semantic segmentation
Hermann Blum, Paul-Edouard Sarlin, Juan I. Nieto, Roland Siegwart, and Cesar Cadena · 2021
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Monte carlo dropblock for modelling uncertainty in object detection
Kumari Deepshikha, Sai Harsha Yelleni, P. K. Srijith, and C. Krishna Mohan · 2021
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Cited alongside, same era.
Deep anomaly detection with outlier exposure
Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich · 2019
Cited alongside, same era.
Evaluating merging strategies for sampling-based uncertainty techniques in object detection
Dimity Miller, Feras Dayoub, Michael Milford, and Niko Sünderhauf · 2019
Cited alongside, same era.
The overlooked elephant of object detection: Open set
Akshay Raj Dhamija, Manuel Günther, Jonathan Ventura, and Terrance E. Boult · 2020
Cited alongside, same era.
Probabilistic object detection: Definition and evaluation
David Hall, Feras Dayoub, John Skinner, Haoyang Zhang, Dimity Miller, Peter Corke, Gustavo Carneiro, Anelia Angelova, and Niko Sünderhauf · 2020
Cited alongside, same era.
Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data
Yen-Chang Hsu, Yilin Shen, Hongxia Jin, and Zsolt Kira · 2020
Cited alongside, same era.
Incremental object detection via meta-learning
K. J. Joseph, Jathushan Rajasegaran, Salman H. Khan, Fahad Shahbaz Khan, Vineeth Balasubramanian, and Ling Shao · 2020
Cited alongside, same era.
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Dense open-set recognition with synthetic outliers generated by real NVP
Matej Grcic, Petra Bevandic, and Sinisa Segvic · 2021
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Estimating and evaluating regression predictive uncertainty in deep object detectors
Ali Harakeh and Steven L. Waslander · 2021
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On the importance of gradients for detecting distributional shifts in the wild
Rui Huang, Andrew Geng, and Yixuan Li · 2021
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Towards open world object detection
K. J. Joseph, Salman Khan, Fahad Shahbaz Khan, and Vineeth N. Balasubramanian · 2021
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Standardized max logits: A simple yet effective approach for identifying unexpected road obstacles in urban-scene segmentation
Sanghun Jung, Jungsoo Lee, Daehoon Gwak, Sungha Choi, and Jaegul Choo · 2021
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Learning open-world object proposals without learning to classify
Dahun Kim, Tsung-Yi Lin, Anelia Angelova, In So Kweon, and Weicheng Kuo · 2021
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Gradient-based quantification of epistemic uncertainty for deep object detectors
Tobias Riedlinger, Matthias Rottmann, Marius Schubert, and Hanno Gottschalk · 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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Robust object detection via instance-level temporal cycle confusion
Xin Wang, Thomas E. Huang, Benlin Liu, Fisher Yu, Xiaolong Wang, Joseph E. Gonzalez, and Trevor Darrell · 2021
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Fine-grained out-of-distribution detection with mixup outlier exposure
Jingyang Zhang, Nathan Inkawhich, Yiran Chen, and Hai Li · 2021
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Zero-shot detection via vision and language knowledge distillation
Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui · 2022
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Provable guarantees for understanding out-of-distribution detection
Peyman Morteza and Yixuan Li · 2022
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