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Linking Neural Collapse and L2 Normalization with Improved
  Out-of-Distribution Detection in Deep Neural Networks
v1v2v3 (latest)

Linking Neural Collapse and L2 Normalization with Improved Out-of-Distribution Detection in Deep Neural Networks

17 September 2022
J. Haas
William Yolland
B. Rabus
    OODD
ArXiv (abs)PDFHTML

Papers citing "Linking Neural Collapse and L2 Normalization with Improved Out-of-Distribution Detection in Deep Neural Networks"

23 / 23 papers shown
Title
Mahalanobis++: Improving OOD Detection via Feature Normalization
Mahalanobis++: Improving OOD Detection via Feature Normalization
Maximilian Mueller
Matthias Hein
OODD
122
1
0
23 May 2025
Double Descent Meets Out-of-Distribution Detection: Theoretical Insights and Empirical Analysis on the role of model complexity
Double Descent Meets Out-of-Distribution Detection: Theoretical Insights and Empirical Analysis on the role of model complexity
Mouin Ben Ammar
David Brellmann
Arturo Mendoza
Antoine Manzanera
Gianni Franchi
OODD
106
0
0
04 Nov 2024
Pursuing Feature Separation based on Neural Collapse for Out-of-Distribution Detection
Pursuing Feature Separation based on Neural Collapse for Out-of-Distribution Detection
Yingwen Wu
Ruiji Yu
Xinwen Cheng
Zhengbao He
Xiaolin Huang
OODD
109
4
0
28 May 2024
An Unconstrained Layer-Peeled Perspective on Neural Collapse
An Unconstrained Layer-Peeled Perspective on Neural Collapse
Wenlong Ji
Yiping Lu
Yiliang Zhang
Zhun Deng
Weijie J. Su
188
87
0
06 Oct 2021
Can convolutional ResNets approximately preserve input distances? A
  frequency analysis perspective
Can convolutional ResNets approximately preserve input distances? A frequency analysis perspective
Lewis Smith
Joost R. van Amersfoort
Haiwen Huang
Stephen J. Roberts
Y. Gal
59
7
0
04 Jun 2021
A Geometric Analysis of Neural Collapse with Unconstrained Features
A Geometric Analysis of Neural Collapse with Unconstrained Features
Zhihui Zhu
Tianyu Ding
Jinxin Zhou
Xiao Li
Chong You
Jeremias Sulam
Qing Qu
75
204
0
06 May 2021
On Feature Collapse and Deep Kernel Learning for Single Forward Pass
  Uncertainty
On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty
Joost R. van Amersfoort
Lewis Smith
Andrew Jesson
Oscar Key
Y. Gal
UQCV
77
104
0
22 Feb 2021
Neural Collapse with Cross-Entropy Loss
Neural Collapse with Cross-Entropy Loss
Jianfeng Lu
Stefan Steinerberger
MLT
86
65
0
15 Dec 2020
Neural collapse with unconstrained features
Neural collapse with unconstrained features
D. Mixon
Hans Parshall
Jianzong Pi
72
121
0
23 Nov 2020
Prevalence of Neural Collapse during the terminal phase of deep learning
  training
Prevalence of Neural Collapse during the terminal phase of deep learning training
Vardan Papyan
Xuemei Han
D. Donoho
216
582
0
18 Aug 2020
Simple and Principled Uncertainty Estimation with Deterministic Deep
  Learning via Distance Awareness
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Jeremiah Zhe Liu
Zi Lin
Shreyas Padhy
Dustin Tran
Tania Bedrax-Weiss
Balaji Lakshminarayanan
UQCVBDL
252
451
0
17 Jun 2020
Robust Out-of-distribution Detection for Neural Networks
Robust Out-of-distribution Detection for Neural Networks
Jiefeng Chen
Yixuan Li
Xi Wu
Yingyu Liang
S. Jha
OODD
199
87
0
21 Mar 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
109
577
0
26 Feb 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke
Sam Gross
Francisco Massa
Adam Lerer
James Bradbury
...
Sasank Chilamkurthy
Benoit Steiner
Lu Fang
Junjie Bai
Soumith Chintala
ODL
568
42,677
0
03 Dec 2019
Can You Trust Your Model's Uncertainty? Evaluating Predictive
  Uncertainty Under Dataset Shift
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Yaniv Ovadia
Emily Fertig
Jie Jessie Ren
Zachary Nado
D. Sculley
Sebastian Nowozin
Joshua V. Dillon
Balaji Lakshminarayanan
Jasper Snoek
UQCV
187
1,706
0
06 Jun 2019
Why ReLU networks yield high-confidence predictions far away from the
  training data and how to mitigate the problem
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
180
559
0
13 Dec 2018
Failing Loudly: An Empirical Study of Methods for Detecting Dataset
  Shift
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
Stephan Rabanser
Stephan Günnemann
Zachary Chase Lipton
70
371
0
29 Oct 2018
A Simple Unified Framework for Detecting Out-of-Distribution Samples and
  Adversarial Attacks
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
Kimin Lee
Kibok Lee
Honglak Lee
Jinwoo Shin
OODD
199
2,064
0
10 Jul 2018
Spreading vectors for similarity search
Spreading vectors for similarity search
Alexandre Sablayrolles
Matthijs Douze
Cordelia Schmid
Hervé Jégou
MQ
127
121
0
08 Jun 2018
SphereFace: Deep Hypersphere Embedding for Face Recognition
SphereFace: Deep Hypersphere Embedding for Face Recognition
Weiyang Liu
Yandong Wen
Zhiding Yu
Ming Li
Bhiksha Raj
Le Song
CVBM
244
2,804
0
26 Apr 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCVBDL
847
5,847
0
05 Dec 2016
A Baseline for Detecting Misclassified and Out-of-Distribution Examples
  in Neural Networks
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Dan Hendrycks
Kevin Gimpel
UQCV
179
3,482
0
07 Oct 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCVBDL
891
9,364
0
06 Jun 2015
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