Multiple Instance Learning with Differential Evolutionary Pooling

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ID: 271477
2021
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Abstract
While implementing Multiple Instance Learning (MIL) through Deep Neural Networks, the most important task is to design the bag-level pooling function that defines the instance-to-bag relationship and eventually determines the class label of a bag. In this article, Differential Evolutionary (DE) pooling—an MIL pooling function based on Differential Evolution (DE) and a bio-inspired metaheuristic—is proposed for the optimization of the instance weights in parallel with training the Deep Neural Network. This article also presents the effects of different parameter adaptation techniques with different variants of DE on MIL.
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bhattacharjee2021electronicsmultiple Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kamanasish Bhattacharjee;Arti Tiwari;Millie Pant;Chang Wook Ahn;Sanghoun Oh;Bhattacharjee, Kamanasish;Tiwari, Arti;Pant, Millie;Ahn, Chang Wook;Oh, Sanghoun;
Journal Electronics
Year 2021
DOI
10.3390/electronics10121403
URL
https://www.mdpi.com/2079-9292/10/12/1403
Keywords
variant adaptive multiple instance learning (mil) differential evolution (de) pooling parameter

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