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。

PDL：使用渐进丢弃层对多实例学习进行规范化