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An Empirical Investigation of Model-to-Model Distribution Shifts in
  Trained Convolutional Filters

An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters

20 January 2022
Paul Gavrikov
J. Keuper
ArXivPDFHTML

Papers citing "An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters"

3 / 3 papers shown
Title
Dissecting U-net for Seismic Application: An In-Depth Study on Deep
  Learning Multiple Removal
Dissecting U-net for Seismic Application: An In-Depth Study on Deep Learning Multiple Removal
Ricard Durall
A. Ghanim
N. Ettrich
J. Keuper
12
2
0
24 Jun 2022
Pre-training without Natural Images
Pre-training without Natural Images
Hirokatsu Kataoka
Kazushige Okayasu
Asato Matsumoto
Eisuke Yamagata
Ryosuke Yamada
Nakamasa Inoue
Akio Nakamura
Y. Satoh
79
117
0
21 Jan 2021
Real-Time Single Image and Video Super-Resolution Using an Efficient
  Sub-Pixel Convolutional Neural Network
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
Wenzhe Shi
Jose Caballero
Ferenc Huszár
J. Totz
Andrew P. Aitken
Rob Bishop
Daniel Rueckert
Zehan Wang
SupR
198
5,176
0
16 Sep 2016
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