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Mitigating severe over-parameterization in deep convolutional neural
  networks through forced feature abstraction and compression with an
  entropy-based heuristic

Mitigating severe over-parameterization in deep convolutional neural networks through forced feature abstraction and compression with an entropy-based heuristic

27 June 2021
Nidhi Gowdra
R. Sinha
Stephen G. MacDonell
W. Yan
ArXivPDFHTML

Papers citing "Mitigating severe over-parameterization in deep convolutional neural networks through forced feature abstraction and compression with an entropy-based heuristic"

3 / 3 papers shown
Title
A Novel Supervised Deep Learning Solution to Detect Distributed Denial
  of Service (DDoS) attacks on Edge Systems using Convolutional Neural Networks
  (CNN)
A Novel Supervised Deep Learning Solution to Detect Distributed Denial of Service (DDoS) attacks on Edge Systems using Convolutional Neural Networks (CNN)
Vedanth Ramanathan
Krish Mahadevan
Sejal Dua
11
2
0
11 Sep 2023
Wider or Deeper: Revisiting the ResNet Model for Visual Recognition
Wider or Deeper: Revisiting the ResNet Model for Visual Recognition
Zifeng Wu
Chunhua Shen
A. Hengel
SSeg
245
1,490
0
30 Nov 2016
Aggregated Residual Transformations for Deep Neural Networks
Aggregated Residual Transformations for Deep Neural Networks
Saining Xie
Ross B. Girshick
Piotr Dollár
Z. Tu
Kaiming He
288
10,214
0
16 Nov 2016
1