A multilayer gated bilinear classifier: From optimizing a deep rectified network to a support vector machine

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

A deep neural network (DNN) is called as a deep rectified network (DRN), if using Rectified Linear Units (ReLUs) as its activation function. In this paper, we show its parameters can be seen to play two important roles simultaneously: one for determining the subnetworks corresponding to the inputs and the other for the parameters of those subnetworks. This observation leads our paper to proposing a method to combine a DNN and an SVM, as a deep classifier. For a DRN trained by a common tuning algorithm, a multilayer gated bilinear classifier is designed to mimic its functionality. Its parameter set is duplicated into two independent sets, playing different roles. One set is used to generate gate signals so as to determine subnetworks corresponding to its inputs, and keeps fixed when optimizing the classifier. The other set serves as parameters of subnetworks, which are linear classifiers. Therefore, their parameters can be implicitly optimized by applying SVM optimizations. Since the DRN is only to generate gate signals, we show in experiments, that it can be trained by using supervised, or unsupervised learning, and even by transfer learning.

本文言語English
ホスト出版物のタイトル2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ140-146
ページ数7
2017-May
ISBN(電子版)9781509061815
DOI
出版ステータスPublished - 2017 6 30
イベント2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
継続期間: 2017 5 142017 5 19

Other

Other2017 International Joint Conference on Neural Networks, IJCNN 2017
CountryUnited States
CityAnchorage
Period17/5/1417/5/19

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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