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

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

26 November 2017
Kimin Lee
Honglak Lee
Kibok Lee
Jinwoo Shin
    OODD
ArXivPDFHTML

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

50 / 174 papers shown
Title
InFlow: Robust outlier detection utilizing Normalizing Flows
InFlow: Robust outlier detection utilizing Normalizing Flows
Nishant Kumar
Pia Hanfeld
Michael Hecht
Michael Bussmann
Stefan Gumhold
Nico Hoffmann
OODD
OOD
TPM
26
4
0
10 Jun 2021
Provably Robust Detection of Out-of-distribution Data (almost) for free
Provably Robust Detection of Out-of-distribution Data (almost) for free
Alexander Meinke
Julian Bitterwolf
Matthias Hein
OODD
25
22
0
08 Jun 2021
Distribution Awareness for AI System Testing
Distribution Awareness for AI System Testing
David Berend
21
8
0
06 May 2021
MOS: Towards Scaling Out-of-distribution Detection for Large Semantic
  Space
MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space
Rui Huang
Yixuan Li
OODD
36
235
0
05 May 2021
MOOD: Multi-level Out-of-distribution Detection
MOOD: Multi-level Out-of-distribution Detection
Ziqian Lin
Sreya . Dutta Roy
Yixuan Li
OODD
26
114
0
30 Apr 2021
Learning to Cascade: Confidence Calibration for Improving the Accuracy
  and Computational Cost of Cascade Inference Systems
Learning to Cascade: Confidence Calibration for Improving the Accuracy and Computational Cost of Cascade Inference Systems
Shohei Enomoto
Takeharu Eda
UQCV
46
17
0
15 Apr 2021
SSD: A Unified Framework for Self-Supervised Outlier Detection
SSD: A Unified Framework for Self-Supervised Outlier Detection
Vikash Sehwag
M. Chiang
Prateek Mittal
OODD
31
330
0
22 Mar 2021
Posterior Meta-Replay for Continual Learning
Posterior Meta-Replay for Continual Learning
Christian Henning
Maria R. Cervera
Francesco DÁngelo
J. Oswald
Regina Traber
Benjamin Ehret
Seijin Kobayashi
Benjamin Grewe
João Sacramento
CLL
BDL
51
54
0
01 Mar 2021
Deep Deterministic Uncertainty: A Simple Baseline
Deep Deterministic Uncertainty: A Simple Baseline
Jishnu Mukhoti
Andreas Kirsch
Joost R. van Amersfoort
Philip H. S. Torr
Y. Gal
UD
UQCV
PER
BDL
24
145
0
23 Feb 2021
Few-shot Conformal Prediction with Auxiliary Tasks
Few-shot Conformal Prediction with Auxiliary Tasks
Adam Fisch
Tal Schuster
Tommi Jaakkola
Regina Barzilay
181
53
0
17 Feb 2021
Bridging In- and Out-of-distribution Samples for Their Better
  Discriminability
Bridging In- and Out-of-distribution Samples for Their Better Discriminability
Engkarat Techapanurak
Anh-Chuong Dang
Takayuki Okatani
OODD
25
3
0
07 Jan 2021
Multidimensional Uncertainty-Aware Evidential Neural Networks
Multidimensional Uncertainty-Aware Evidential Neural Networks
Yibo Hu
Yuzhe Ou
Xujiang Zhao
Jin-Hee Cho
Feng Chen
EDL
UQCV
AAML
25
23
0
26 Dec 2020
Task-Adaptive Negative Envision for Few-Shot Open-Set Recognition
Task-Adaptive Negative Envision for Few-Shot Open-Set Recognition
Shiyuan Huang
Jiawei Ma
G. Han
Shih-Fu Chang
BDL
33
19
0
24 Dec 2020
Entropy Maximization and Meta Classification for Out-Of-Distribution
  Detection in Semantic Segmentation
Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic Segmentation
Robin Shing Moon Chan
Matthias Rottmann
Hanno Gottschalk
OODD
37
149
0
09 Dec 2020
The Hidden Uncertainty in a Neural Networks Activations
The Hidden Uncertainty in a Neural Networks Activations
Janis Postels
Hermann Blum
Yannick Strümpler
Cesar Cadena
Roland Siegwart
Luc Van Gool
Federico Tombari
UQCV
30
22
0
05 Dec 2020
Feature Space Singularity for Out-of-Distribution Detection
Feature Space Singularity for Out-of-Distribution Detection
Haiwen Huang
Zhihan Li
Lulu Wang
Sishuo Chen
Bin Dong
Xinyu Zhou
OODD
20
65
0
30 Nov 2020
Energy-based Out-of-distribution Detection
Energy-based Out-of-distribution Detection
Weitang Liu
Xiaoyun Wang
John Douglas Owens
Yixuan Li
OODD
71
1,290
0
08 Oct 2020
A Wholistic View of Continual Learning with Deep Neural Networks:
  Forgotten Lessons and the Bridge to Active and Open World Learning
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning
Martin Mundt
Yongjun Hong
Iuliia Pliushch
Visvanathan Ramesh
CLL
27
146
0
03 Sep 2020
On the Structures of Representation for the Robustness of Semantic
  Segmentation to Input Corruption
On the Structures of Representation for the Robustness of Semantic Segmentation to Input Corruption
Charles Lehman
Dogancan Temel
Ghassan AlRegib
19
4
0
02 Sep 2020
A Survey of Visual Analytics Techniques for Machine Learning
A Survey of Visual Analytics Techniques for Machine Learning
Jun Yuan
Changjian Chen
Weikai Yang
Mengchen Liu
Jiazhi Xia
Shixia Liu
21
216
0
21 Aug 2020
Beyond Point Estimate: Inferring Ensemble Prediction Variation from
  Neuron Activation Strength in Recommender Systems
Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems
Zhe Chen
Yuyan Wang
Dong Lin
D. Cheng
Lichan Hong
Ed H. Chi
Claire Cui
28
16
0
17 Aug 2020
Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning
Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning
Qing Yu
Daiki Ikami
Go Irie
Kiyoharu Aizawa
12
128
0
22 Jul 2020
Detecting Out-of-distribution Samples via Variational Auto-encoder with
  Reliable Uncertainty Estimation
Detecting Out-of-distribution Samples via Variational Auto-encoder with Reliable Uncertainty Estimation
Xuming Ran
Mingkun Xu
Lingrui Mei
Qi Xu
Quanying Liu
OODD
UQCV
39
50
0
16 Jul 2020
Contrastive Training for Improved Out-of-Distribution Detection
Contrastive Training for Improved Out-of-Distribution Detection
Jim Winkens
Rudy Bunel
Abhijit Guha Roy
Robert Stanforth
Vivek Natarajan
...
Alan Karthikesalingam
Simon A. A. Kohl
taylan. cemgil
S. M. Ali Eslami
Olaf Ronneberger
OODD
11
234
0
10 Jul 2020
Distribution-free binary classification: prediction sets, confidence
  intervals and calibration
Distribution-free binary classification: prediction sets, confidence intervals and calibration
Chirag Gupta
A. Podkopaev
Aaditya Ramdas
UQCV
27
79
0
18 Jun 2020
AI Research Considerations for Human Existential Safety (ARCHES)
AI Research Considerations for Human Existential Safety (ARCHES)
Andrew Critch
David M. Krueger
22
50
0
30 May 2020
Few-Shot Open-Set Recognition using Meta-Learning
Few-Shot Open-Set Recognition using Meta-Learning
Bo Liu
Hao Kang
Haoxiang Li
G. Hua
Nuno Vasconcelos
BDL
EDL
20
89
0
27 May 2020
SCOUT: Self-aware Discriminant Counterfactual Explanations
SCOUT: Self-aware Discriminant Counterfactual Explanations
Pei Wang
Nuno Vasconcelos
FAtt
22
81
0
16 Apr 2020
Towards Inheritable Models for Open-Set Domain Adaptation
Towards Inheritable Models for Open-Set Domain Adaptation
Jogendra Nath Kundu
Naveen Venkat
R. Ambareesh
V. RahulM.
R. Venkatesh Babu
VLM
17
117
0
09 Apr 2020
Regularizing Class-wise Predictions via Self-knowledge Distillation
Regularizing Class-wise Predictions via Self-knowledge Distillation
Sukmin Yun
Jongjin Park
Kimin Lee
Jinwoo Shin
27
274
0
31 Mar 2020
Hybrid Models for Open Set Recognition
Hybrid Models for Open Set Recognition
Hongjie Zhang
Ang Li
Jie Guo
Yanwen Guo
BDL
20
184
0
27 Mar 2020
Adversarial Robustness on In- and Out-Distribution Improves
  Explainability
Adversarial Robustness on In- and Out-Distribution Improves Explainability
Maximilian Augustin
Alexander Meinke
Matthias Hein
OOD
75
98
0
20 Mar 2020
Anomalous Example Detection in Deep Learning: A Survey
Anomalous Example Detection in Deep Learning: A Survey
Saikiran Bulusu
B. Kailkhura
Bo-wen Li
P. Varshney
D. Song
AAML
28
47
0
16 Mar 2020
An Empirical Evaluation on Robustness and Uncertainty of Regularization
  Methods
An Empirical Evaluation on Robustness and Uncertainty of Regularization Methods
Sanghyuk Chun
Seong Joon Oh
Sangdoo Yun
Dongyoon Han
Junsuk Choe
Y. Yoo
AAML
OOD
329
53
0
09 Mar 2020
Utilizing Network Properties to Detect Erroneous Inputs
Utilizing Network Properties to Detect Erroneous Inputs
Matt Gorbett
Nathaniel Blanchard
AAML
16
6
0
28 Feb 2020
Generalized ODIN: Detecting Out-of-distribution Image without Learning
  from Out-of-distribution Data
Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data
Yen-Chang Hsu
Yilin Shen
Hongxia Jin
Z. Kira
OODD
24
558
0
26 Feb 2020
The Conditional Entropy Bottleneck
The Conditional Entropy Bottleneck
Ian S. Fischer
OOD
19
115
0
13 Feb 2020
OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples
OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples
Changjian Chen
Jun Yuan
Yafeng Lu
Yang Liu
Hang Su
Songtao Yuan
Shixia Liu
OODD
13
63
0
08 Feb 2020
On-manifold Adversarial Data Augmentation Improves Uncertainty
  Calibration
On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration
Kanil Patel
William H. Beluch
Dan Zhang
Michael Pfeiffer
Bin Yang
UQCV
19
30
0
16 Dec 2019
Confidence Calibration and Predictive Uncertainty Estimation for Deep
  Medical Image Segmentation
Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation
Alireza Mehrtash
W. Wells
C. Tempany
Purang Abolmaesumi
Tina Kapur
OOD
FedML
UQCV
24
262
0
29 Nov 2019
Scaling Out-of-Distribution Detection for Real-World Settings
Scaling Out-of-Distribution Detection for Real-World Settings
Dan Hendrycks
Steven Basart
Mantas Mazeika
Andy Zou
Joe Kwon
Mohammadreza Mostajabi
Jacob Steinhardt
D. Song
OODD
15
455
0
25 Nov 2019
Deep Verifier Networks: Verification of Deep Discriminative Models with
  Deep Generative Models
Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models
Tong Che
Xiaofeng Liu
Site Li
Yubin Ge
Ruixiang Zhang
Caiming Xiong
Yoshua Bengio
33
52
0
18 Nov 2019
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
J. Liu
John Paisley
M. Kioumourtzoglou
B. Coull
UQCV
UD
PER
24
83
0
11 Nov 2019
Identifying Unknown Instances for Autonomous Driving
Identifying Unknown Instances for Autonomous Driving
K. Wong
Shenlong Wang
Mengye Ren
Ming Liang
R. Urtasun
22
110
0
24 Oct 2019
Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an
  Early-Layer Output
Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output
Vahdat Abdelzad
Krzysztof Czarnecki
Rick Salay
Taylor Denouden
Sachin Vernekar
Buu Phan
OODD
19
45
0
23 Oct 2019
Out-of-distribution Detection in Classifiers via Generation
Out-of-distribution Detection in Classifiers via Generation
Sachin Vernekar
Ashish Gaurav
Vahdat Abdelzad
Taylor Denouden
Rick Salay
Krzysztof Czarnecki
OODD
19
83
0
09 Oct 2019
Open Set Medical Diagnosis
Open Set Medical Diagnosis
Viraj Prabhu
A. Kannan
Geoffrey Tso
Namit Katariya
Manish Chablani
David Sontag
X. Amatriain
21
9
0
07 Oct 2019
Addressing Failure Prediction by Learning Model Confidence
Addressing Failure Prediction by Learning Model Confidence
Charles Corbière
Nicolas Thome
Avner Bar-Hen
Matthieu Cord
P. Pérez
24
281
0
01 Oct 2019
Towards neural networks that provably know when they don't know
Towards neural networks that provably know when they don't know
Alexander Meinke
Matthias Hein
OODD
22
139
0
26 Sep 2019
Out-of-domain Detection for Natural Language Understanding in Dialog
  Systems
Out-of-domain Detection for Natural Language Understanding in Dialog Systems
Yinhe Zheng
Guanyi Chen
Minlie Huang
18
121
0
09 Sep 2019
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