Is decaf good enough for accurate image classification?

Yajuan Cai, Guoqiang Zhong*, Yuchen Zheng, Kaizhu Huang, Junyu Dong

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

4 Citations (Scopus)


In recent years, deep learning has attracted much interest for addressing complex AI tasks. However, most of the deep learning models need to be trained for a long time in order to obtain good results. To overcome this problem, the deep convolutional activation feature (DeCAF) was proposed, which is directly extracted from the activation of a well trained deep convolutional neural network. Nevertheless, the dimensionality of DeCAF is simply fixed to a constant number. In this case, one may ask whether DeCAF is good enough for image classification applications and whether we can further improve its performance? To answer these two questions, we propose a new model called RS-DeCAF based on “reducing” and “stretching” the dimensionality of DeCAF. In the implementation of RS-DeCAF, we reduce the dimensionality of DeCAF using dimensionality reduction methods, such as PCA, and meanwhile increase the dimensionality by stretching the weight matrix between successive layers. RS-DeCAF is aimed to discover the effective representations of data for classification tasks. As there is no back propagation is needed for network training, RS-DeCAF is very efficient and can be easily applied to large scale problems. Extensive experiments on image classification show that RS-DeCAF not only slightly im proves DeCAF, but dramatically outperforms previous “stretching” and other state-ofthe-art approaches. Hence, RS-DeCAF can be considered as an effective substitute for previous DeCAF and “stretching” approaches.

Original languageEnglish
Title of host publicationNeural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings
EditorsWeng Kin Lai, Qingshan Liu, Tingwen Huang, Sabri Arik
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319265346
Publication statusPublished - 2015
Event22nd International Conference on Neural Information Processing, ICONIP 2015 - Istanbul, Turkey
Duration: 9 Nov 201512 Nov 2015

Publication series

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


Conference22nd International Conference on Neural Information Processing, ICONIP 2015


  • DeCAF
  • Deep convolutional neural network
  • Feature learning
  • Image classification
  • Stretching


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