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2007.13723
Cited By

MaxDropout: Deep Neural Network Regularization Based on Maximum Output
Values

MaxDropout: Deep Neural Network Regularization Based on Maximum Output Values

International Conference on Pattern Recognition (ICPR), 2020

27 July 2020

C. F. G. Santos

João Paulo Papa

ArXiv (abs)PDF HTML

Papers citing "MaxDropout: Deep Neural Network Regularization Based on Maximum Output Values"

5 / 5 papers shown

The Overfocusing Bias of Convolutional Neural Networks: A
Saliency-Guided Regularization Approach

The Overfocusing Bias of Convolutional Neural Networks: A Saliency-Guided Regularization Approach

Eduardo Hugo Sanchez

Emmanuel Rachelson

241

1

0

25 Sep 2024

Advancing Spiking Neural Networks towards Multiscale Spatiotemporal
Interaction Learning

Advancing Spiking Neural Networks towards Multiscale Spatiotemporal Interaction Learning

Nhan Duy Truong

294

11

0

22 May 2024

LayerCollapse: Adaptive compression of neural networks

LayerCollapse: Adaptive compression of neural networks

Soheil Zibakhsh Shabgahi

Mohammad Soheil Shariff

210

1

0

29 Nov 2023

MaxDropoutV2: An Improved Method to Drop out Neurons in Convolutional
Neural Networks

MaxDropoutV2: An Improved Method to Drop out Neurons in Convolutional Neural NetworksIberian Conference on Pattern Recognition and Image Analysis (IbPRIA), 2022

C. F. G. Santos

João Paulo Papa

145

1

0

05 Mar 2022

Avoiding Overfitting: A Survey on Regularization Methods for
Convolutional Neural Networks

Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural NetworksACM Computing Surveys (ACM CSUR), 2022

C. F. G. Santos

João Paulo Papa

197

309

0

10 Jan 2022