Multiple Instance Learning with Differential Evolutionary Pooling

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.