多示例学习算法综述

doi:10.11896/jsjkx.210500047

Abstract

Abstract: Multi-instance learning(MIL) is a typical weakly supervised learning framework,where every training example,called bag,is a set of instances.Since the learning process of an MIL algorithm depends on only the labels of bags rather than those of any individual instances,MIL can fit well with applications in which instance labels are difficult to get.Recently,deep multi-instance learning methods attract widespread attention,so deep MIL has become a major research focus.This paper reviews some research progress of MIL.Firstly,MIL algorithms are divided into shallow and deep models according to their hierarchical structure.Secondly,various algorithms are reviewed and summarized in these two categories,and then different pooling methods of deep MIL models are analyzed.Moreover,the fundamental theorem of symmetric functions for models with set-type data as training samples and its application in deep MIL are expounded.Finally,the performance of different algorithms is compared and analyzed through experiments,and their interpretability is analyzed thoroughly.After that,problems to be further investigated are discussed.

Key words: Deep learning, Fundamental theorem of symmetric functions, Interpretability, Machine learning, Multi-instance learning, Multi-instance pooling

CLC Number:

TP181

ZHAO Lu, YUAN Li-ming, HAO Kun. Review of Multi-instance Learning Algorithms[J].Computer Science, 2022, 49(6A): 93-99.

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