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Can convolutional ResNets approximately preserve input distances? A
  frequency analysis perspective

Can convolutional ResNets approximately preserve input distances? A frequency analysis perspective

4 June 2021
Lewis Smith
Joost R. van Amersfoort
Haiwen Huang
Stephen J. Roberts
Y. Gal
ArXivPDFHTML

Papers citing "Can convolutional ResNets approximately preserve input distances? A frequency analysis perspective"

4 / 4 papers shown
Title
ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step
  Inference
ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference
N. Durasov
Nik Dorndorf
Hieu M. Le
Pascal Fua
UQCV
27
10
0
21 Nov 2022
SAFE: Sensitivity-Aware Features for Out-of-Distribution Object
  Detection
SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection
Samuel Wilson
Tobias Fischer
Feras Dayoub
Dimity Miller
Niko Sünderhauf
OODD
26
29
0
29 Aug 2022
A Simple Approach to Improve Single-Model Deep Uncertainty via
  Distance-Awareness
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
J. Liu
Shreyas Padhy
Jie Jessie Ren
Zi Lin
Yeming Wen
Ghassen Jerfel
Zachary Nado
Jasper Snoek
Dustin Tran
Balaji Lakshminarayanan
UQCV
BDL
16
48
0
01 May 2022
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
1