Multiple instance learning (MIL) was a weakly supervised learning approach
that sought to assign binary class labels to collections of instances known as
bags. However, due to their weak supervision nature, the MIL methods were
susceptible to overfitting and required assistance in developing comprehensive
representations of target instances. While regularization typically effectively
combated overfitting, its integration with the MIL model has been frequently
overlooked in prior studies. Meanwhile, current regularization methods for MIL
have shown limitations in their capacity to uncover a diverse array of
representations. In this study, we delve into the realm of regularization
within the MIL model, presenting a novel approach in the form of a Progressive
Dropout Layer (PDL). We aim to not only address overfitting but also empower
the MIL model in uncovering intricate and impactful feature representations.
The proposed method was orthogonal to existing MIL methods and could be easily
integrated into them to boost performance. Our extensive evaluation across a
range of MIL benchmark datasets demonstrated that the incorporation of the PDL
into multiple MIL methods not only elevated their classification performance
but also augmented their potential for weakly-supervised feature localizations.

本研究提出了一种新的正则化方法，即渐进性丢失层（PDL），旨在解决弱监督学习中过拟合问题，并增强多实例学习模型在发现复杂和有影响力的特征表示方面的能力。通过在多个多实例学习方法中集成 PDL，我们在各种基准数据集上进行了广泛的评估，结果显示 PDL 的整合不仅提高了分类性能，还增强了模型进行弱监督特征定位的潜力。