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To facilitate reliable deployments of autonomous robots in the real world, Out-of-Distribution (OOD) detection capabilities are often required.
The information bottleneck method
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B. Lakshminarayanan, A. Pritzel, and C. Blundell · 2017
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D. P. Kingma and P. Dhariwal · 2018
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Do deep generative models know what they don’t know?
E. Nalisnick, A. Matsukawa, Y. W. Teh, D. Gorur, and B. Lakshminarayanan · 2018
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C.-W. Huang, D. Krueger, A. Lacoste, and A. Courville · 2018
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W. Grathwohl, R. T. Chen, J. Bettencourt, I. Sutskever, and D. Duvenaud · 2018
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D. Miller, L. Nicholson, F. Dayoub, and N. Sünderhauf · 2018
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
K. Lee, K. Lee, H. Lee, and J. Shin · 2018
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E. Fetaya, J.-H. Jacobsen, W. Grathwohl, and R. Zemel · 2019
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M. Bauer and A. Mnih · 2019
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R. T. Chen, J. Behrmann, D. K. Duvenaud, and J.-H. Jacobsen · 2019
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A rad approach to deep mixture models
L. Dinh, J. Sohl-Dickstein, H. Larochelle, and R. Pascanu · 2019
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Likelihood ratios for out-of-distribution detection
J. Ren, P. J. Liu, E. Fertig, J. Snoek, R. Poplin, M. Depristo, J. Dillon, and B. Lakshminarayanan · 2019
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Detecting out-of-distribution inputs to deep generative models using typicality
E. Nalisnick, A. Matsukawa, Y. W. Teh, and B. Lakshminarayanan · 2019
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Hybrid models with deep and invertible features
E. Nalisnick, A. Matsukawa, Y. W. Teh, D. Gorur, and B. Lakshminarayanan · 2019
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The overlooked elephant of object detection: Open set
A. Dhamija, M. Gunther, J. Ventura, and T. Boult · 2020
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Why normalizing flows fail to detect out-of-distribution data
P. Kirichenko, P. Izmailov, and A. G. Wilson · 2020
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Hybrid models for open set recognition
H. Zhang, A. Li, J. Guo, and Y. Guo · 2020
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Relaxing bijectivity constraints with continuously indexed normalising flows
R. Cornish, A. Caterini, G. Deligiannidis, and A. Doucet · 2020
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Stochastic normalizing flows
H. Wu, J. Köhler, and F. Noé · 2020
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Natural posterior network: Deep bayesian uncertainty for exponential family distributions
B. Charpentier, O. Borchert, D. Zügner, S. Geisler, and S. Günnemann · 2021
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Generalized out-of-distribution detection: A survey
J. Yang, K. Zhou, Y. Li, and Z. Liu · 2021
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A simple fix to mahalanobis distance for improving near-ood detection
J. Ren, S. Fort, J. Liu, A. G. Roy, S. Padhy, and B. Lakshminarayanan · 2021
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Trust your robots! predictive uncertainty estimation of neural networks with sparse gaussian processes
J. Lee, J. Feng, M. Humt, M. G. Müller, and R. Triebel · 2022
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Bayesian active learning for sim-to-real robotic perception
J. Feng, J. Lee, M. Durner, and R. Triebel · 2022
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Tails of lipschitz triangular flows
P. Jaini, I. Kobyzev, Y. Yu, and M. Brubaker · 2020
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Training normalizing flows with the information bottleneck for competitive generative classification
L. Ardizzone, R. Mackowiak, C. Rother, and U. Köthe · 2020
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Normalizing flows: An introduction and review of current methods
I. Kobyzev, S. J. Prince, and M. A. Brubaker · 2020
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Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts
B. Charpentier, D. Zügner, and S. Günnemann · 2020
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The hidden uncertainty in a neural networks activations
J. Postels, H. Blum, Y. Strümpler, C. Cadena, R. Siegwart, L. Van Gool, and F. Tombari · 2020
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Survae flows: Surjections to bridge the gap between vaes and flows
D. Nielsen, P. Jaini, E. Hoogeboom, O. Winther, and M. Welling · 2020
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A system-level view on out-of-distribution data in robotics
R. Sinha, A. Sharma, S. Banerjee, T. Lew, R. Luo, S. M. Richards, Y. Sun, E. Schmerling, and M. Pavone · 2022
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Out-of-distribution identification: Let detector tell which i am not sure
R. Li, C. Zhang, H. Zhou, C. Shi, and Y. Luo · 2022
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Resampling base distributions of normalizing flows
V. Stimper, B. Schölkopf, and J. M. Hernández-Lobato · 2022
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Mitigating neural network overconfidence with logit normalization
H. Wei, R. Xie, H. Cheng, L. Feng, B. An, and Y. Li · 2022
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Revisiting flow generative models for out-of-distribution detection
D. Jiang, S. Sun, and Y. Yu · 2022
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Safe output feedback motion planning from images via learned perception modules and contraction theory
G. Chou, N. Ozay, and D. Berenson · 2022
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Deep hybrid models for out-of-distribution detection
S. Cao and Z. Zhang · 2022
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Introspective robot perception using smoothed predictions from bayesian neural networks
J. Feng, M. Durner, Z.-C. Márton, F. Bálint-Benczédi, and R. Triebel · 2022
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SIREN: Shaping representations for detecting out-of-distribution objects
X. Du, G. Gozum, Y. Ming, and Y. Li · 2022
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Unknown-aware object detection: Learning what you don’t know from videos in the wild
X. Du, X. Wang, G. Gozum, and Y. Li · 2022
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Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao · 2023
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Out-of-distribution detection for adaptive computer vision
S. K. Lind, R. Triebel, L. Nardi, and V. Krueger · 2023
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Training, architecture, and prior for deterministic uncertainty methods
B. Charpentier, C. Zhang, and S. Günnemann · 2023
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Density-based feasibility learning with normalizing flows for introspective robotic assembly
J. Feng, M. Atad, I. V. Rodriguez Brena, M. Durner, and R. Triebel · 2023
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A survey of uncertainty in deep neural networks
J. Gawlikowski, C. R. N. Tassi, M. Ali, J. Lee, M. Humt, J. Feng, A. Kruspe, R. Triebel, P. Jung, R. Roscher, et al · 2023
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Normalizing flow based feature synthesis for outlier-aware object detection
N. Kumar, S. Šegvić, A. Eslami, and S. Gumhold · 2023
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