NDLI: Multi-task deep neural network for multi-label learning

Content Provider	IEEE Xplore Digital Library
Author	Yan Huang Wei Wang Liang Wang Tieniu Tan
Copyright Year	2013
Description	Author affiliation: Nat. Lab. of Pattern Recognition, Inst. of Autom., Beijing, China (Yan Huang; Wei Wang; Liang Wang; Tieniu Tan)
Abstract	This paper proposes a multi-task deep neural network (MT-DNN) architecture to handle the multi-label learning problem, in which each label learning is defined as a binary classification task, i.e., a positive class for “an instance owns this label” and a negative class for “an instance does not own this label”. Multi-label learning is accordingly transformed to multiple binary-class classification tasks. Considering that a deep neural nets (DNN) architecture can learn good intermediate representations shared across tasks, we generalize one classification task of traditional DNN into multiple binary classification tasks through defining the output layer with a negative class node and a positive class node for each label. After a similar pretraining process to deep belief nets, we redefine the label assignment error of MT-DNN and perform the back-propagation algorithm to fine-tune the network. To evaluate the proposed model, we carry out image annotation experiments on two public image datasets, with 2000 images and 30,000 images respectively. The experiments demonstrate that the proposed model achieves the state-of-the-art performance.
Starting Page	2897
Ending Page	2900
File Size	1243903
Page Count	4
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738596
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image Annotation Multi-Task Learning Deep Neural Network Multi-Label Learning
Content Type	Text
Resource Type	Article

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