MILDMS: Multiple Instance Learning via DD Constraint and Multiple Part Similarity

MILDMS: Multiple Instance Learning via DD Constraint and Multiple Part Similarity

As a subject area of symmetry, multiple instance learning (MIL) is a special form of a weakly supervised learning problem where the label is related to the bag, not the instances contained in it. The difficulty of MIL lies in the incomplete label information of instances. To resolve this problem, in...

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Bibliographic Details
Main Authors:	Chao Wen, Zhan Li, Jian Qu, Qingchen Fan, Aiping Li
Format:	Article
Language:	English
Published:	MDPI AG 2019-08-01
Series:	Symmetry
Subjects:	multiple instance learning convolutional neural networks (CNN) DD constraint multiple part similarity
Online Access:	https://www.mdpi.com/2073-8994/11/9/1080

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