Address Instance-level Label Prediction in Multiple Instance Learning

TL;DR

This paper introduces a novel MIL algorithm that predicts instance labels directly, with an unbiased loss estimation, outperforming existing methods and approaching fully supervised model performance.

Contribution

The paper proposes a new instance-level loss for MIL that can be unbiasedly estimated without instance labels, enabling better instance-level prediction.

Findings

Achieves superior instance-level performance compared to state-of-the-art MIL methods.

Can estimate instance labels without using instance labels during training.

Performs comparably to fully supervised models for instance label prediction.

Abstract

\textit{Multiple Instance Learning} (MIL) is concerned with learning from bags of instances, where only bag labels are given and instance labels are unknown. Existent approaches in this field were mainly designed for the bag-level label prediction (predict labels for bags) but not the instance-level (predict labels for instances), with the task loss being only defined at the bag level. This restricts their application in many tasks, where the instance-level labels are more interested. In this paper, we propose a novel algorithm, whose loss is specifically defined at the instance level, to address instance-level label prediction in MIL. We prove that the loss of this algorithm can be unbiasedly and consistently estimated without using instance labels, under the i.i.d assumption. Empirical study validates the above statements and shows that the proposed algorithm can achieve superior instance-level and comparative bag-level performance, compared to state-of-the-art MIL methods. In addition, it shows that the proposed method can achieve similar results as the fully supervised model (trained with instance labels) for label prediction at the instance level.