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Depthwise Separable Convolutions Allow for Fast and Memory-Efficient
  Spectral Normalization

Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization

12 February 2021
Christina Runkel
Christian Etmann
Michael Möller
Carola-Bibiane Schönlieb
ArXivPDFHTML

Papers citing "Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization"

3 / 3 papers shown
Title
Meta Pseudo Labels
Meta Pseudo Labels
Hieu H. Pham
Zihang Dai
Qizhe Xie
Minh-Thang Luong
Quoc V. Le
VLM
262
658
0
23 Mar 2020
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train
  10,000-Layer Vanilla Convolutional Neural Networks
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
Lechao Xiao
Yasaman Bahri
Jascha Narain Sohl-Dickstein
S. Schoenholz
Jeffrey Pennington
244
349
0
14 Jun 2018
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision
  Applications
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Andrew G. Howard
Menglong Zhu
Bo Chen
Dmitry Kalenichenko
Weijun Wang
Tobias Weyand
M. Andreetto
Hartwig Adam
3DH
950
20,599
0
17 Apr 2017
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