Publication Type
Journal Article
Version
publishedVersion
Publication Date
1-2018
Abstract
In this work, we propose a novel deep neural network referred to as Multi-Target Deep Neural Network (MT-DNN). We theoretically prove that different stable target models with shared learning paths are stable and can achieve optimal solutions respectively. Based on GoogleNet, we design a single model with three different targets, one for classification, one for regression, and one for masks that is composed of 256 × 256 sub-models. Unlike bounding boxes used in ImageNet, our single model can draw the shapes of target objects, and in the meanwhile, classify the objects and calculate their sizes. We validate our single MT-DNN model via rigorous experiments and prove that the multiple targets can boost each other to achieve optimization solutions.
Keywords
Object detection, Segmentation, Learning path, Multi-target deep learning, Deep neural networks
Discipline
Databases and Information Systems | OS and Networks
Research Areas
Data Science and Engineering
Publication
Neurocomputing
Volume
273
First Page
634
Last Page
642
ISSN
0925-2312
Identifier
10.1016/j.neucom.2017.08.044
Publisher
Elsevier
Citation
ZENG, Zeng; LIANG, Nanying; YANG, Xulei; and HOI, Steven C. H..
Multi-target deep neural networks: Theoretical analysis and implementation. (2018). Neurocomputing. 273, 634-642.
Available at: https://ink.library.smu.edu.sg/sis_research/4200
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.neucom.2017.08.044