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Automated driving has become a major topic of interest not only in the active research community but also in mainstream media reports.
“The Earth Mover’s Distance as a Metric for Image Retrieval,”
Yossi Rubner, Carlo Tomasi, and Leonidas J. Guibas, · 2000
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“Probability Models for Open Set Recognition,”
W. J. Scheirer, L. P. Jain, and T. E. Boult, · 2014
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“Faster R-CNN: Towards Real-Time Object Detection With Region Proposal Networks,”
S. Ren, K. He, R. Girshick, and J. Sun, · 2015
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“U-Net: Convolutional Networks for Biomedical Image Segmentation,”
Olaf Ronneberger, Philipp Fischer, and Thomas Brox, · 2015
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“Learning Discriminative Reconstructions for Unsupervised Outlier Removal,”
Y. Xia, X. Cao, F. Wen, G. Hua, and J. Sun, · 2015
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“Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding,”
A. Kendall, V. Badrinarayanan, and R. Cipolla, · 2015
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“Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles,”
P. Pinggera, S. Ramos, S. Gehrig, U. Franke, C. Rother, and R. Mester, · 2016
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“Learning Temporal Regularity in Video Sequences,”
M. Hasan, J. Choi, J. Neumann, A. K. Roy-Chowdhury, and L. S. Davis, · 2016
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“Towards Open Set Deep Networks,”
A. Bendale and T. Boult, · 2016
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“Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning,”
Y. Gal and Z. Ghahramani, · 2016
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“Mask R-CNN,”
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick, · 2017
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“Detecting Unexpected Obstacles for Self-Driving Cars: Fusing Deep Learning and Geometric Modeling,”
S. Ramos, S. Gehring, P. Pinggera, U. Franke, and C. Rother, · 2017
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“Categorization of Anomalies in Smart Manufacturing Systems to Support the Selection of Detection Mechanisms,”
F. Lopez, M. Saez, Y. Shao, E. C. Balta, J. Moyne, Z. M. Mao, K. Barton, and D. Tilbury, · 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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“What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?,”
A. Kendall and Y. Gal, · 2017
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“Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles,”
B. Lakshminarayanan, A. Pritzel, and C. Blundell, · 2017
Cited alongside, same era.
“ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation,”
Eduardo Romera, José M. Álvarez, Luis M. Bergasa, and Roberto Arroyo, · 2018
Cited alongside, same era.
“Future Frame Prediction for Anomaly Detection – A New Baseline,”
W. Liu, W. Luo, D. Lian, and S. Gao, · 2018
Cited alongside, same era.
“Training Confidence-Calibrated Classifiers for Detecting Out-of-Distribution Samples,”
Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin, · 2018
Cited alongside, same era.
“Wasserstein Distance Guided Representation Learning for Domain Adaptation,”
J. Shen, Y. Qu, W. Zhang, and Y. Yu, · 2018
Cited alongside, same era.
“Enhancing the Reliability of Out-Of-Distribution Image Detection in Neural Networks,”
“Towards Corner Case Detection for Autonomous Driving,”
J.-A. Bolte, A. Bär, D. Lipinski, and T. Fingscheidt, · 2019
Later among the works it cites.
“Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection,”
D. Gong, L. Liu, Vuong Le, B. Saha, M. R. Mansour, S. Venkatesh, and A. van den Hengel, · 2019
Later among the works it cites.
“C2AE: Class Conditioned Auto-Encoder for Open-set Recognition,”
P. Oza and V. M. Patel, · 2019
Later among the works it cites.
“Why ReLU Networks Yield High-Confidence Predictions Far Away From The Training Data And How To Mitigate The Problem,”
M. Hein, M. Andriushchenko, and J. Bitterwolf, · 2019
Later among the works it cites.
“Detecting the Unexpected via Image Resynthesis,”
K. Lis, K. Nakka, P. Fua, and M. Salzmann, · 2019
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“Group Anomaly Detection Using Deep Generative Models,”
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S. Liang, Y. Li, and R. Srikant, · 2018
Cited alongside, same era.
“ODN: Open Deep Network for Open-Set Action Recognition,”
Yu Shu, Yemin Shi, Yaowei Wang, Yixiong Zou, Qingsheng Yuan, and Yonghong Tian, · 2018
Cited alongside, same era.
“Learning Confidence for Out-of-Distribution Detection in Neural Networks,”
Terrance DeVries and Graham W. Taylor, · 2018
Cited alongside, same era.
“Deep Anomaly Detection Using Geometric Transformations,”
I. Golan and R. El-Yaniv, · 2018
Cited alongside, same era.
“Bayesian Semantic Instance Segmentation in Open Set World,”
T. Pham, V. B. G. Kumar, T.-T. Do, G. Carneiro, and I. Reid, · 2018
Cited alongside, same era.
“Efficient Uncertainty Estimation for Semantic Segmentation in Videos,”
P.-Y. Huang, W.-T. Hsu, C.-Y. Chiu, T.-F. Wu, and M. Sun, · 2018
Cited alongside, same era.
“Deep One-Class Classification,”
L. Ruff, R. Vandermeulen, N. Goernitz, L. Deecke, S. A. Siddiqui, A. Binder, E. Müller, and M. Kloft, · 2018
Cited alongside, same era.
R. Chalapathy, E. Toth, and S. Chawla, · 2019
Later among the works it cites.
“Benchmarking Neural Network Robustness to Common Corruptions and Perturbations,”
Dan Hendrycks and Thomas Dietterich, · 2019
Later among the works it cites.
“Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection,”
B. Barz, E. Rodner, Y. G. Garcia, and J. Denzler, · 2019
Later among the works it cites.
“Classification-Reconstruction Learning for Open-Set Recognition,”
R. Yoshihashi, W. Shao, R. Kawakami, S. You, M. Iida, and T. Naemura, · 2019
Later among the works it cites.
“Unsupervised Domain Adaptation to Improve Image Segmentation Quality Both in the Source and Target Domain,”
Jan-Aike Bolte, Markus Kamp, Antonia Breuer, Silviu Homoceanu, Peter Schlicht, Fabian Hüger, Daniel Lipinski, and Tim Fingscheidt, · 2019
Later among the works it cites.
“Systematization of Corner Cases for Visual Perception in Automated Driving,”
Jasmin Breitenstein, Jan-Aike Termöhlen, Daniel Lipinski, and Tim Fingscheidt, · 2020
Later among the works it cites.
“Self-Supervised Domain Mismatch Estimation for Autonomous Perception,”
Jonas Löhdefink, Justin Fehrling, Marvin Klingner, Fabian Hüger, Peter Schlicht, Nico M. Schmidt, and Tim Fingscheidt, · 2020
Later among the works it cites.
“Distance-Based Learning from Errors for Confidence Calibration,”
Chen Xing, Sercan Arik, Zizhao Zhang, and Tomas Pfister, · 2020
Later among the works it cites.
Joost van Amersfoort, Lewis Smith, Yee Whye Teh, and Yarin Gal, · 2020
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“A Deep-Learning Approach for the Detection of Overexposure in Automotive Camera Images,”
I. Jatzkowski, D. Wilke, and M. Maurer, · 2035
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