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Testing the Efficient Network TRaining (ENTR) Hypothesis: initially
  reducing training image size makes Convolutional Neural Network training for
  image recognition tasks more efficient

Testing the Efficient Network TRaining (ENTR) Hypothesis: initially reducing training image size makes Convolutional Neural Network training for image recognition tasks more efficient

30 July 2018
T. Wanger
Peter Frohn
ArXivPDFHTML

Papers citing "Testing the Efficient Network TRaining (ENTR) Hypothesis: initially reducing training image size makes Convolutional Neural Network training for image recognition tasks more efficient"

3 / 3 papers shown
Title
Identifying Cocoa Pollinators: A Deep Learning Dataset
Identifying Cocoa Pollinators: A Deep Learning Dataset
Wenxiu Xu
Saba Ghorbani Bazegar
Dong Sheng
Manuel Toledo-Hernandez
ZhenZhong Lan
Thomas Cherico Wanger
28
0
0
31 Dec 2024
Wider or Deeper: Revisiting the ResNet Model for Visual Recognition
Wider or Deeper: Revisiting the ResNet Model for Visual Recognition
Zifeng Wu
Chunhua Shen
Anton Van Den Hengel
SSeg
260
1,491
0
30 Nov 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,145
0
06 Jun 2015
1