Field effect deep networks for image recognition with incomplete data

Sheng Hua Zhong, Yan Liu, Kien A. Hu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)


� 2016 ACM. Image recognition with incomplete data is a well-known hard problem in computer vision and machine learning. This article proposes a novel deep learning technique called Field Effect Bilinear Deep Networks (FEBDN) for this problem. To address the difficulties of recognizing incomplete data, we design a novel second-order deep architecture with the Field Effect Restricted Boltzmann Machine, which models the reliability of the delivered information according to the availability of the features. Based on this new architecture, we propose a new three-stage learning procedure with field effect bilinear initialization, field effect abstraction and estimation, and global fine-tuning with missing features adjustment. By integrating the reliability of features into the new learning procedure, the proposed FEBDN can jointly determine the classification boundary and estimate the missing features. FEBDN has demonstrated impressive performance on recognition and estimation tasks in various standard datasets.
Original languageEnglish
Article number52
JournalACM Transactions on Multimedia Computing, Communications and Applications
Issue number4
Publication statusPublished - 1 Aug 2016


  • Deep learning
  • Image recognition
  • Incomplete data
  • Missing features

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications


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