  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.
