Fetching the paper…
Reading the bibliography…
Supervised classification methods often assume the train and test data distributions are the same and that all classes in the test set are present in the training set.
An optimum character recognition system using decision functions
C.-K. Chow · 1957
Earlier work this paper cites.
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach
E. R. DeLong, D. M. DeLong, and D. L. Clarke-Pearson · 1988
Earlier work this paper cites.
Algorithms for mining distancebased outliers in large datasets
E. M. Knox and R. T. Ng · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Support vector method for novelty detection
B. Schölkopf, R. C. Williamson, A. J. Smola, J. Shawe-Taylor, and J. C. Platt · 2000
Earlier work this paper cites.
Outlier detection for high dimensional data
C. C. Aggarwal and P. S. Yu · 2001
Earlier work this paper cites.
Estimating the support of a high-dimensional distribution
B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson · 2001
Earlier work this paper cites.
Fast outlier detection in high dimensional spaces
F. Angiulli and C. Pizzuti · 2002
Earlier work this paper cites.
Mining distance-based outliers in near linear time with randomization and a simple pruning rule
S. D. Bay and M. Schwabacher · 2003
Earlier work this paper cites.
Classification with reject option
R. Herbei and M. H. Wegkamp · 2006
Earlier work this paper cites.
Caltech-256 object category dataset, 2007
G. Griffin, A. Holub, and P. Perona · 2007
Earlier work this paper cites.
Classification with a reject option using a hinge loss
P. L. Bartlett and M. H. Wegkamp · 2008
Earlier work this paper cites.
On the foundations of noise-free selective classification
R. El-Yaniv and Y. Wiener · 2010
Earlier work this paper cites.
Vod: A novel outlier detection algorithm based on voronoi diagram
W. Qin and J. Qu · 2010
Earlier work this paper cites.
Meta-recognition: The theory and practice of recognition score analysis
W. J. Scheirer, A. Rocha, R. J. Micheals, and T. E. Boult · 2011
Earlier work this paper cites.
Machine learning: a probabilistic perspective
K. P. Murphy · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
Earlier work this paper cites.
Towards open set recognition
W. J. Scheirer, A. Rocha, A. Sapkota, and T. E. Boult · 2013
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
Cited alongside, same era.
Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Cited alongside, same era.
Probability models for open set recognition
W. J. Scheirer, L. P. Jain, and T. E. Boult · 2014
Cited alongside, same era.
Deepface: Closing the gap to human-level performance in face verification
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf · 2014
Cited alongside, same era.
How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
Cited alongside, same era.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Open-category classification by adversarial sample generation
Y. Yu, W.-Y. Qu, N. Li, and Z. Guo · 2017
Later among the works it cites.
Learning confidence for out-of-distribution detection in neural networks
T. DeVries and G. Taylor · 2018
Later among the works it cites.
Reducing network agnostophobia
A. R. Dhamija, M. Günther, and T. Boult · 2018
Later among the works it cites.
Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 2018
Later among the works it cites.
Fearnet: Brain-inspired model for incremental learning
R. Kemker and C. Kanan · 2018
Later among the works it cites.
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
K. Lee, K. Lee, H. Lee, and J. Shin · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Nguyen, J. Yosinski, and J. Clune · 2015
Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Cited alongside, same era.
Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
Cited alongside, same era.
Towards open set deep networks
A. Bendale and T. E. Boult · 2016
Cited alongside, same era.
High-dimensional and large-scale anomaly detection using a linear one-class svm with deep learning
S. M. Erfani, S. Rajasegarar, S. Karunasekera, and C. Leckie · 2016
Cited alongside, same era.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Y. Gal and Z. Ghahramani · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Later among the works it cites.
Enhancing the reliability of out-of-distribution image detection in neural networks
S. Liang, Y. Li, and R. Srikant · 2018
Later among the works it cites.
Open set learning with counterfactual images
L. Neal, M. Olson, X. Fern, W.-K. Wong, and F. Li · 2018
Later among the works it cites.
Unseen class discovery in open-world classification
L. Shu, H. Xu, and B. Liu · 2018
Later among the works it cites.
Out-of-distribution detection using an ensemble of self supervised leave-out classifiers
A. Vyas, N. Jammalamadaka, X. Zhu, D. Das, B. Kaul, and T. L. Willke · 2018
Later among the works it cites.
Memory efficient experience replay for streaming learning
T. L. Hayes, N. D. Cahill, and C. Kanan · 2019
Closest in time.
REMIND your neural network to prevent catastrophic forgetting, 2019
T. L. Hayes, K. Kafle, R. Shrestha, M. Acharya, and C. Kanan · 2019
Closest in time.
Benchmarking neural network robustness to common corruptions and perturbations
D. Hendrycks and T. Dietterich · 2019
Closest in time.
Deep anomaly detection with outlier exposure
D. Hendrycks, M. Mazeika, and T. Dietterich · 2019
Closest in time.
Do deep generative models know what they don’t know?
E. Nalisnick, A. Matsukawa, Y. W. Teh, D. Gorur, and B. Lakshminarayanan · 2019
Closest in time.
Deep cnn-based multi-task learning for open-set recognition
P. Oza and V. M. Patel · 2019
Closest in time.
Continual lifelong learning with neural networks: A review
G. I. Parisi, R. Kemker, J. L. Part, C. Kanan, and S. Wermter · 2019
Closest in time.
Learning deep features for one-class classification
P. Perera and V. M. Patel · 2019
Closest in time.