Description: We focus on the Multi-Instance learning with Emerging Novel class (MIEN) problem, and formulate MIEN from a metric learning perspective. We extract key instances to form the "super-bag" for each observed class, and non-key instances from all the observed classes to form a "meta super-bag". Based on these super-bags, we propose the MIEN-metric method to learn discriminative metrics for classifying MIL bags from the observed classes and recognizing bags from the novel class.
References:
[1] Xiu-Shen Wei, Han-Jia Ye, Xin Mu, Jianxin Wu, Chunhua Shen, Zhi-Hua Zhou. Multi-Instance Learning with Emerging Novel Class. IEEE Transactions on Knowledge and Data Engineering, 2019, in press.
ATTN: This package is free for academic usage. You can run it at your own risk. For other purposes, please contact
Prof. Zhi-Hua Zhou.
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