Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples

26 November 2017

Papers citing "Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples"

24 / 174 papers shown

Title
Density estimation in representation space to predict model uncertainty Tiago Ramalho M. Corbalan UQCV BDL 11 37 0 20 Aug 2019
Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy Qing Yu Kiyoharu Aizawa OODD 11 163 0 14 Aug 2019
Natural Adversarial Examples Dan Hendrycks Kevin Zhao Steven Basart Jacob Steinhardt D. Song OODD 50 1,419 0 16 Jul 2019
Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data Simao Eduardo A. Nazábal Christopher K. I. Williams Charles Sutton DRL 11 32 0 15 Jul 2019
Likelihood Ratios for Out-of-Distribution Detection Jie Jessie Ren Peter J. Liu Emily Fertig Jasper Snoek Ryan Poplin M. DePristo Joshua V. Dillon Balaji Lakshminarayanan OODD 18 716 0 07 Jun 2019
Practical Deep Learning with Bayesian Principles Kazuki Osawa S. Swaroop Anirudh Jain Runa Eschenhagen Richard Turner Rio Yokota Mohammad Emtiyaz Khan BDL UQCV 56 240 0 06 Jun 2019
Analysis of Confident-Classifiers for Out-of-distribution Detection Sachin Vernekar Ashish Gaurav Taylor Denouden Buu Phan Vahdat Abdelzad Rick Salay Krzysztof Czarnecki OODD 18 18 0 27 Apr 2019
Out-of-Distribution Detection for Generalized Zero-Shot Action Recognition Devraj Mandal Sanath Narayan Sai Kumar Dwivedi Vikram Gupta Shuaib Ahmed F. Khan Ling Shao OODD 14 141 0 18 Apr 2019
Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild Kibok Lee Kimin Lee Jinwoo Shin Honglak Lee CLL 29 201 0 29 Mar 2019
Deep CNN-based Multi-task Learning for Open-Set Recognition Poojan Oza Vishal M. Patel 16 35 0 07 Mar 2019
Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN Ke Sun Zhanxing Zhu Zhouchen Lin AAML 17 20 0 28 Feb 2019
The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning Benjamin J. Meyer Tom Drummond 14 33 0 27 Feb 2019
Using Pre-Training Can Improve Model Robustness and Uncertainty Dan Hendrycks Kimin Lee Mantas Mazeika NoLa 17 717 0 28 Jan 2019
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem Matthias Hein Maksym Andriushchenko Julian Bitterwolf OODD 40 552 0 13 Dec 2018
Deep Anomaly Detection with Outlier Exposure Dan Hendrycks Mantas Mazeika Thomas G. Dietterich OODD 31 1,449 0 11 Dec 2018
Building robust classifiers through generation of confident out of distribution examples K. Sricharan Ashok Srivastava OOD 8 31 0 01 Dec 2018
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift Stephan Rabanser Stephan Günnemann Zachary Chase Lipton 27 357 0 29 Oct 2018
Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers Apoorv Vyas Nataraj Jammalamadaka Xia Zhu Dipankar Das Bharat Kaul Theodore L. Willke OODD 14 246 0 04 Sep 2018
Controlling Over-generalization and its Effect on Adversarial Examples Generation and Detection Mahdieh Abbasi Arezoo Rajabi A. Mozafari R. Bobba Christian Gagné AAML 16 9 0 21 Aug 2018
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks Kimin Lee Kibok Lee Honglak Lee Jinwoo Shin OODD 21 1,994 0 10 Jul 2018
Hierarchical Novelty Detection for Visual Object Recognition Kibok Lee Kimin Lee Kyle Min Y. Zhang Jinwoo Shin Honglak Lee BDL 44 67 0 02 Apr 2018
Learning Confidence for Out-of-Distribution Detection in Neural Networks Terrance Devries Graham W. Taylor OOD OODD 15 581 0 13 Feb 2018
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,661 0 05 Dec 2016
Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 266 7,636 0 03 Jul 2012