Fetching the paper…
Reading the bibliography…
Deep convolutional models often produce inadequate predictions for inputs foreign to the training distribution.
The pascal visual object classes (voc) challenge,
M. Everingham, L. Gool, C. K. Williams, J. Winn, A. Zisserman, · 2010
Earlier work this paper cites.
Probability models for open set recognition,
W. Scheirer, L. Jain, T. Boult, · 2014
Earlier work this paper cites.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop,
F. Yu, Y. Zhang, S. Song, A. Seff, J. Xiao, · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge,
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, L. Fei-Fei, · 2015
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding,
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, B. Schiele, · 2016
Earlier work this paper cites.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning,
Y. Gal, Z. Ghahramani, · 2016
Earlier work this paper cites.
Lost and found: detecting small road hazards for self-driving vehicles,
P. Pinggera, S. Ramos, S. Gehrig, U. Franke, C. Rother, R. Mester, · 2016
Earlier work this paper cites.
The mapillary vistas dataset for semantic understanding of street scenes,
G. Neuhold, T. Ollmann, S. R. Bulò, P. Kontschieder, · 2017
Earlier work this paper cites.
Scene parsing through ade20k dataset,
B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, A. Torralba, · 2017
Earlier work this paper cites.
What uncertainties do we need in bayesian deep learning for computer vision?,
A. Kendall, Y. Gal, · 2017
Earlier work this paper cites.
Understanding deep learning requires rethinking generalization,
C. Zhang, S. Bengio, M. Hardt, B. Recht, O. Vinyals, · 2017
Earlier work this paper cites.
A baseline for detecting misclassified and out-of-distribution examples in neural networks,
D. Hendrycks, K. Gimpel, · 2017
Earlier work this paper cites.
On calibration of modern neural networks,
C. Guo, G. Pleiss, Y. Sun, K. Q. Weinberger, · 2017
Earlier work this paper cites.
Pyramid scene parsing network,
H. Zhao, J. Shi, X. Qi, X. Wang, J. Jia, · 2017
Earlier work this paper cites.
Ladder-style densenets for semantic segmentation of large natural images,
I. Kreso, J. Krapac, S. Segvic, · 2017
Earlier work this paper cites.
Dilated residual networks,
F. Yu, V. Koltun, T. Funkhouser, · 2017
Earlier work this paper cites.
In-place activated batchnorm for memory-optimized training of dnns,
S. R. Bulò, L. Porzi, P. Kontschieder, · 2017
Cited alongside, same era.
Wilddash - creating hazard-aware benchmarks,
O. Zendel, K. Honauer, M. Murschitz, D. Steininger, G. Fernandez Dominguez, · 2018
Cited alongside, same era.
Discriminative out-of-distribution detection for semantic segmentation,
P. Bevandic, I. Kreso, M. Orsic, S. Segvic, · 2018
Cited alongside, same era.
Enhancing the reliability of out-of-distribution image detection in neural networks,
S. Liang, Y. Li, R. Srikant, · 2018
Cited alongside, same era.
Learning confidence for out-of-distribution detection in neural networks,
T. DeVries, G. W. Taylor, · 2018
Cited alongside, same era.
Simultaneous semantic segmentation and outlier detection in presence of domain shift,
P. Bevandic, I. Kreso, M. Orsic, S. Segvic, · 2019
Later among the works it cites.
Aleatoric and epistemic uncertainty in machine learning: A tutorial introduction,
E. Hüllermeier, W. Waegeman, · 2019
Later among the works it cites.
Deep anomaly detection with outlier exposure,
D. Hendrycks, M. Mazeika, T. Dietterich, · 2019
Later among the works it cites.
Learning deep features for one-class classification,
P. Perera, V. M. Patel, · 2019
Later among the works it cites.
Efficacy of pixel-level OOD detection for semantic segmentation,
M. Angus, K. Czarnecki, R. Salay, · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Understanding measures of uncertainty for adversarial example detection,
L. Smith, Y. Gal, · 2018
Cited alongside, same era.
Predictive uncertainty estimation via prior networks,
A. Malinin, M. Gales, · 2018
Cited alongside, same era.
Training confidence-calibrated classifiers for detecting out-of-distribution samples,
K. Lee, H. Lee, K. Lee, J. Shin, · 2018
Cited alongside, same era.
Adversarially learned anomaly detection,
H. Zenati, M. Romain, C. Foo, B. Lecouat, V. Chandrasekhar, · 2018
Cited alongside, same era.
Encoder-decoder with atrous separable convolution for semantic image segmentation,
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, H. Adam, · 2018
Cited alongside, same era.
Training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation,
P. Meletis, G. Dubbelman, · 2018
Cited alongside, same era.
Deep gated attention networks for large-scale street-level scene segmentation,
P. Zhang, W. Liu, H. Wang, Y. Lei, H. Lu, · 2019
Cited alongside, same era.
R. Zhu, S. Zhang, X. Wang, L. Wen, H. Shi, L. Bo, T. Mei, · 2019
Later among the works it cites.
Synthesize then compare: Detecting failures and anomalies for semantic segmentation,
Y. Xia, Y. Zhang, F. Liu, W. Shen, A. Yuille, · 2020
Later among the works it cites.
Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings,
P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, · 2020
Later among the works it cites.
Hybrid models for open set recognition,
H. Zhang, A. Li, J. Guo, Y. Guo, · 2020
Later among the works it cites.
Your classifier is secretly an energy based model and you should treat it like one,
W. Grathwohl, K. Wang, J. Jacobsen, D. Duvenaud, M. Norouzi, K. Swersky, · 2020
Later among the works it cites.
Multi-head enhanced self-attention network for novelty detection,
Y. Zhang, Y. Gong, H. Zhu, X. Bai, W. Tang, · 2020
Later among the works it cites.
MSeg: A composite dataset for multi-domain semantic segmentation,
J. Lambert, L. Zhuang, O. Sener, J. Hays, V. Koltun, · 2020
Later among the works it cites.
Efficient ladder-style densenets for semantic segmentation of large images,
I. Krešo, J. Krapac, S. Segvic, · 2020
Later among the works it cites.
One versus all for deep neural network incertitude (OVNNI) quantification,
G. Franchi, A. Bursuc, E. Aldea, S. Dubuisson, I. Bloch, · 2020
Later among the works it cites.
Tradi: Tracking deep neural network weight distributions,
G. Franchi, A. Bursuc, E. Aldea, S. Dubuisson, I. Bloch, · 2020
Later among the works it cites.