Deep MIML Network

Authors

  • Ji Feng Nanjing University
  • Zhi-Hua Zhou Nanjing University

DOI:

https://doi.org/10.1609/aaai.v31i1.10890

Keywords:

deep learning, multi-instance multi-label learning

Abstract

In many real world applications, the concerned objects are with multiple labels, and can be represented as a bag of instances. Multi-instance Multi-label (MIML) learning provides a framework for handling such task and has exhibited excellent performance in various domains. In a MIML setting, the feature representation of instances usually has big impact on the final performance; inspired by the recent deep learning studies, in this paper, we propose the DeepMIML network which exploits deep neural network formation to generate instance representation for MIML. The sub-concept learning component of the DeepMIML structure reserves the instance-label relation discovery ability of MIML algorithms; that is, it can automatically locating the key input patterns that trigger the labels. The effectiveness of DeepMIML network is validated by experiments on various domains of data.

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Published

2017-02-13

How to Cite

Feng, J., & Zhou, Z.-H. (2017). Deep MIML Network. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.10890