Field Effect Deep Networks For Image Recognition With Incomplete Data

Keywords

Deep learning; Image recognition; Incomplete data; Missing features

Abstract

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.

Publication Date

8-1-2016

Publication Title

ACM Transactions on Multimedia Computing, Communications and Applications

Volume

12

Issue

4

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/2957754

Socpus ID

84983048011 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84983048011

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