通过调整注意力与预测概率的边界，提升部分标签多实例学习的性能。
Multi-Instance Partial-Label Learning with Margin Adjustment
- 引入感知边界的注意力机制，动态调节注意力得分的边界。
- 设计边界分布损失，使候选标签预测概率高于非候选标签。
- 在多个数据集上优于现有多实例与部分标签学习方法。
多实例部分标签学习（MIPL）是一种新兴的学习框架，其中每个训练样本以包含候选标签集的多实例包形式表示。现有MIPL算法常忽略注意力得分与预测概率的边界，导致泛化性能不佳。关键问题在于分类器的最高预测概率可能出现在非候选标签上。本文提出MIPLMA（带边界调整的多实例部分标签学习）算法，通过调整注意力得分与预测概率的边界来优化性能。我们引入一种感知边界的注意力机制，动态调节注意力得分的边界，并提出边界分布损失，约束候选标签集与非候选标签集间预测概率的边界。实验表明，MIPLMA在多个数据集上均显著优于现有的MIPL算法，以及其它成熟的多实例学习和部分标签学习算法。
原文摘要 · Abstract (English)
Multi-instance partial-label learning (MIPL) is an emerging learning framework where each training sample is represented as a multi-instance bag associated with a candidate label set. Existing MIPL algorithms often overlook the margins for attention scores and predicted probabilities, leading to suboptimal generalization performance. A critical issue with these algorithms is that the highest prediction probability of the classifier may appear on a non-candidate label. In this paper, we propose an algorithm named MIPLMA, i.e., Multi-Instance Partial-Label learning with Margin Adjustment, which adjusts the margins for attention scores and predicted probabilities. We introduce a margin-aware attention mechanism to dynamically adjust the margins for attention scores and propose a margin distribution loss to constrain the margins between the predicted probabilities on candidate and non-candidate label sets. Experimental results demonstrate the superior performance of MIPLMA over existing MIPL algorithms, as well as other well-established multi-instance learning algorithms and partial-label learning algorithms.
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