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CNN Filter DB: An Empirical Investigation of Trained Convolutional
  Filters
v1v2 (latest)

CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters

Computer Vision and Pattern Recognition (CVPR), 2022
29 March 2022
Paul Gavrikov
J. Keuper
    AAML
ArXiv (abs)PDFHTMLGithub (32★)

Papers citing "CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters"

21 / 21 papers shown
Fuzzy-Pattern Tsetlin Machine
Fuzzy-Pattern Tsetlin Machine
Artem Hnilov
127
1
0
11 Aug 2025
Convolutional Neural Networks Do Work with Pre-Defined Filters
Convolutional Neural Networks Do Work with Pre-Defined FiltersIEEE International Joint Conference on Neural Network (IJCNN), 2023
C. Linse
Erhardt Barth
T. Martinetz
353
6
0
27 Nov 2024
ResiDual Transformer Alignment with Spectral Decomposition
ResiDual Transformer Alignment with Spectral Decomposition
Lorenzo Basile
Valentino Maiorca
Luca Bortolussi
Emanuele Rodolà
Francesco Locatello
706
6
0
31 Oct 2024
Enhancing Generalization in Convolutional Neural Networks through
  Regularization with Edge and Line Features
Enhancing Generalization in Convolutional Neural Networks through Regularization with Edge and Line FeaturesInternational Conference on Artificial Neural Networks (ICANN), 2024
C. Linse
Beatrice Brückner
Thomas Martinetz
201
1
0
22 Oct 2024
How Do Training Methods Influence the Utilization of Vision Models?
How Do Training Methods Influence the Utilization of Vision Models?
Paul Gavrikov
Shashank Agnihotri
Margret Keuper
J. Keuper
392
2
0
18 Oct 2024
Unveiling the Unseen: Identifiable Clusters in Trained Depthwise
  Convolutional Kernels
Unveiling the Unseen: Identifiable Clusters in Trained Depthwise Convolutional KernelsInternational Conference on Learning Representations (ICLR), 2024
Z. Babaiee
Peyman M. Kiasari
Daniela Rus
Radu Grosu
201
8
0
25 Jan 2024
SecurityNet: Assessing Machine Learning Vulnerabilities on Public Models
SecurityNet: Assessing Machine Learning Vulnerabilities on Public Models
Boyang Zhang
Zheng Li
Ziqing Yang
Xinlei He
Michael Backes
Mario Fritz
Yang Zhang
372
10
0
19 Oct 2023
On the Interplay of Convolutional Padding and Adversarial Robustness
On the Interplay of Convolutional Padding and Adversarial Robustness
Paul Gavrikov
J. Keuper
AAML
395
4
0
12 Aug 2023
Sparsified Model Zoo Twins: Investigating Populations of Sparsified
  Neural Network Models
Sparsified Model Zoo Twins: Investigating Populations of Sparsified Neural Network Models
D. Honegger
Konstantin Schurholt
Damian Borth
290
5
0
26 Apr 2023
An Extended Study of Human-like Behavior under Adversarial Training
An Extended Study of Human-like Behavior under Adversarial Training
Paul Gavrikov
J. Keuper
Margret Keuper
AAML
289
12
0
22 Mar 2023
Revisiting Hidden Representations in Transfer Learning for Medical
  Imaging
Revisiting Hidden Representations in Transfer Learning for Medical Imaging
Dovile Juodelyte
Amelia Jiménez-Sánchez
Veronika Cheplygina
OOD
345
2
0
16 Feb 2023
The Power of Linear Combinations: Learning with Random Convolutions
The Power of Linear Combinations: Learning with Random Convolutions
Paul Gavrikov
J. Keuper
354
3
0
26 Jan 2023
GoogLe2Net: Going Transverse with Convolutions
GoogLe2Net: Going Transverse with Convolutions
Yuanpeng He
249
2
0
01 Jan 2023
On the Transferability of Visual Features in Generalized Zero-Shot
  Learning
On the Transferability of Visual Features in Generalized Zero-Shot Learning
Paola Cascante-Bonilla
Leonid Karlinsky
James Smith
Yanjun Qi
Vicente Ordonez
328
2
0
22 Nov 2022
Does Medical Imaging learn different Convolution Filters?
Does Medical Imaging learn different Convolution Filters?
Paul Gavrikov
J. Keuper
VLMOOD
149
2
0
25 Oct 2022
Model Zoos: A Dataset of Diverse Populations of Neural Network Models
Model Zoos: A Dataset of Diverse Populations of Neural Network ModelsNeural Information Processing Systems (NeurIPS), 2022
Konstantin Schurholt
Diyar Taskiran
Boris Knyazev
Xavier Giró-i-Nieto
Damian Borth
409
40
0
29 Sep 2022
E Pluribus Unum Interpretable Convolutional Neural Networks
E Pluribus Unum Interpretable Convolutional Neural NetworksScientific Reports (Sci Rep), 2022
George Dimas
Eirini Cholopoulou
D. Iakovidis
313
6
0
10 Aug 2022
Adversarial Robustness through the Lens of Convolutional Filters
Adversarial Robustness through the Lens of Convolutional Filters
Paul Gavrikov
J. Keuper
180
15
0
05 Apr 2022
On the Privacy Risks of Deploying Recurrent Neural Networks in Machine
  Learning Models
On the Privacy Risks of Deploying Recurrent Neural Networks in Machine Learning Models
Yunhao Yang
Parham Gohari
Ufuk Topcu
AAML
362
3
0
06 Oct 2021
YOLO9000: Better, Faster, Stronger
YOLO9000: Better, Faster, StrongerComputer Vision and Pattern Recognition (CVPR), 2016
Joseph Redmon
Ali Farhadi
VLMObjD
828
17,440
0
25 Dec 2016
Xception: Deep Learning with Depthwise Separable Convolutions
Xception: Deep Learning with Depthwise Separable ConvolutionsComputer Vision and Pattern Recognition (CVPR), 2016
François Chollet
MDEBDLPINN
3.6K
17,433
0
07 Oct 2016
1
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