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Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on
  Semantic Segmentation

Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation

26 October 2023
Kira Maag
Asja Fischer
    AAML
    SSeg
ArXivPDFHTML

Papers citing "Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation"

8 / 8 papers shown
Title
Uni-SLAM: Uncertainty-Aware Neural Implicit SLAM for Real-Time Dense
  Indoor Scene Reconstruction
Uni-SLAM: Uncertainty-Aware Neural Implicit SLAM for Real-Time Dense Indoor Scene Reconstruction
Shaoxiang Wang
Yaxu Xie
Chun-Peng Chang
Christen Millerdurai
A. Pagani
Didier Stricker
67
1
0
29 Nov 2024
Detecting Adversarial Attacks in Semantic Segmentation via Uncertainty
  Estimation: A Deep Analysis
Detecting Adversarial Attacks in Semantic Segmentation via Uncertainty Estimation: A Deep Analysis
Kira Maag
Roman Resner
Asja Fischer
AAML
42
0
0
19 Aug 2024
Reducing Texture Bias of Deep Neural Networks via Edge Enhancing
  Diffusion
Reducing Texture Bias of Deep Neural Networks via Edge Enhancing Diffusion
Edgar Heinert
Matthias Rottmann
Kira Maag
Karsten Kahl
17
6
0
14 Feb 2024
Pixel-wise Gradient Uncertainty for Convolutional Neural Networks
  applied to Out-of-Distribution Segmentation
Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation
Kira Maag
Tobias Riedlinger
UQCV
38
7
0
13 Mar 2023
Certified Defences Against Adversarial Patch Attacks on Semantic
  Segmentation
Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation
Maksym Yatsura
K. Sakmann
N. G. Hua
Matthias Hein
J. H. Metzen
AAML
52
17
0
13 Sep 2022
The Vulnerability of Semantic Segmentation Networks to Adversarial
  Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing
The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing
Andreas Bär
Jonas Löhdefink
Nikhil Kapoor
Serin Varghese
Fabian Hüger
Peter Schlicht
Tim Fingscheidt
AAML
108
33
0
11 Jan 2021
Adversarial Machine Learning at Scale
Adversarial Machine Learning at Scale
Alexey Kurakin
Ian Goodfellow
Samy Bengio
AAML
296
3,112
0
04 Nov 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
UQCV
BDL
285
9,145
0
06 Jun 2015
1