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An Empirical Evaluation of Current Convolutional Architectures' Ability to Manage Nuisance Location and Scale Variability
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An Empirical Evaluation of Current Convolutional Architectures' Ability to Manage Nuisance Location and Scale Variability

Computer Vision and Pattern Recognition (CVPR), 2015
Nikolaos Karianakis
Jingming Dong
Stefano Soatto
ArXiv (abs)PDFHTML

Papers citing "An Empirical Evaluation of Current Convolutional Architectures' Ability to Manage Nuisance Location and Scale Variability"

7 / 7 papers shown
Title
Investigating Weight-Perturbed Deep Neural Networks With Application in Iris Presentation Attack Detection
Investigating Weight-Perturbed Deep Neural Networks With Application in Iris Presentation Attack Detection
Renu Sharma
Redwan Sony
Arun Ross
AAML
165
3
0
21 Nov 2023
Model-Based Robust Deep Learning: Generalizing to Natural, Out-of-Distribution Data
Model-Based Robust Deep Learning: Generalizing to Natural, Out-of-Distribution Data
Avi Schwarzschild
Hamed Hassani
George J. Pappas
OOD
204
43
0
20 May 2020
Empirical Upper Bound, Error Diagnosis and Invariance Analysis of Modern Object Detectors
Empirical Upper Bound, Error Diagnosis and Invariance Analysis of Modern Object Detectors
Ali Borji
ObjD
90
3
0
05 Apr 2020
Assessing Capsule Networks With Biased Data
Assessing Capsule Networks With Biased Data
Bruno Ferrarini
Shoaib Ehsan
Adrien Bartoli
A. Leonardis
Klaus D. McDonald-Maier
76
2
0
09 Apr 2019
With Friends Like These, Who Needs Adversaries?
With Friends Like These, Who Needs Adversaries?
Saumya Jetley
Nicholas A. Lord
Juil Sock
AAML
238
71
0
11 Jul 2018
Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems
Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems
Adam Kortylewski
Bernhard Egger
Andreas C. Schneider
Thomas Gerig
Andreas Morel-Forster
T. Vetter
CVBM
244
71
0
05 Dec 2017
Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches
Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches
E. Rodner
Marcel Simon
Robert B. Fisher
Joachim Denzler
116
40
0
21 Oct 2016

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