Clustering-Based Adaptive Dropout for CNN-Based Classification

Zhiwei Wen, Zhiwei Ke, Weicheng Xie, Linlin Shen

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review


Dropout has been widely used to improve the generalization ability of a deep network, while current dropout variants rarely adapt the dropout probabilities of the network hidden units or weights dynamically to their contributions on the network optimization. In this work, a clustering-based dropout based on the network characteristics of features, weights or their derivatives is proposed, where the dropout probabilities for these characteristics are updated self-adaptively according to the corresponding clustering group to differentiate their contributions. Experimental results on the databases of Fashion-MNIST and CIFAR10 and expression databases of FER2013 and CK+ show that the proposed clustering-based dropout achieves better accuracy than the original dropout and various dropout variants, and the most competitive performances compared with state-of-the-art algorithms.

Original languageEnglish
Title of host publicationPattern Recognition - 5th Asian Conference, ACPR 2019, Revised Selected Papers
EditorsShivakumara Palaiahnakote, Gabriella Sanniti di Baja, Liang Wang, Wei Qi Yan
Number of pages13
ISBN (Print)9783030414030
Publication statusPublished - 2020
Externally publishedYes
Event5th Asian Conference on Pattern Recognition, ACPR 2019 - Auckland, New Zealand
Duration: 26 Nov 201929 Nov 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12046 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference5th Asian Conference on Pattern Recognition, ACPR 2019
Country/TerritoryNew Zealand


  • Facial expression recognition
  • Feature and weight clustering
  • Feature derivative dropout
  • Self-adaptive dropout probability

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


Dive into the research topics of 'Clustering-Based Adaptive Dropout for CNN-Based Classification'. Together they form a unique fingerprint.

Cite this