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Studying Invariances of Trained Convolutional Neural Networks

Studying Invariances of Trained Convolutional Neural Networks

15 March 2018
Charlotte Bunne
Lukas Rahmann
Thomas Wolf
ArXiv (abs)PDFHTML

Papers citing "Studying Invariances of Trained Convolutional Neural Networks"

4 / 4 papers shown
Title
Invariance Measures for Neural Networks
Invariance Measures for Neural Networks
F. Quiroga
J. Torrents-Barrena
Laura Lanzarini
Domenec Puig-Valls
31
4
0
26 Oct 2023
Inspect, Understand, Overcome: A Survey of Practical Methods for AI
  Safety
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
Sebastian Houben
Stephanie Abrecht
Maram Akila
Andreas Bär
Felix Brockherde
...
Serin Varghese
Michael Weber
Sebastian J. Wirkert
Tim Wirtz
Matthias Woehrle
AAML
128
58
0
29 Apr 2021
A Self-Supervised Feature Map Augmentation (FMA) Loss and Combined
  Augmentations Finetuning to Efficiently Improve the Robustness of CNNs
A Self-Supervised Feature Map Augmentation (FMA) Loss and Combined Augmentations Finetuning to Efficiently Improve the Robustness of CNNs
Nikhil Kapoor
C. Yuan
Jonas Löhdefink
Roland S. Zimmermann
Serin Varghese
Fabian Hüger
Nico M. Schmidt
Peter Schlicht
Tim Fingscheidt
AAML
39
4
0
02 Dec 2020
Why do deep convolutional networks generalize so poorly to small image
  transformations?
Why do deep convolutional networks generalize so poorly to small image transformations?
Aharon Azulay
Yair Weiss
108
563
0
30 May 2018
1