重新思考多示例学习：通过弱监督自训练开发实例级分类器
Rethinking Multiple Instance Learning: Developing an Instance-Level Classifier via Weakly-Supervised Self-Training
AI总结：
本文将多示例学习重构为半监督实例分类，提出带全局与局部伪标签约束的弱监督自训练方法，迫使分类器学习困难正实例，并在多类基准上达到SOTA。
摘要：
多示例学习（MIL）问题目前要么从包分类角度、要么从实例分类角度求解，两者都忽略了部分实例所包含的重要信息，导致性能受限。例如，现有方法往往难以学习困难正实例。本文将MIL表述为一个半监督实例分类问题，从而能够充分利用所有有标签和无标签实例来训练更好的分类器。这一表述的困难在于，在MIL中所有有标签实例都是负实例，而半监督学习中使用的传统自训练技术在此场景下为无标签实例生成伪标签时往往会退化。为解决该问题，我们提出一种弱监督自训练方法，其中利用正包标签对伪标签构建全局约束和局部约束，以防止其退化，并迫使分类器学习困难正实例。值得注意的是，简单正实例是指在分类过程中远离决策边界的实例，而困难正实例则是靠近决策边界的实例。通过迭代优化，伪标签能够逐渐逼近真实标签。在两个MNIST合成数据集、五个传统MIL基准数据集和两个组织病理学全切片图像数据集上的大量实验表明，我们的方法在所有数据集上均取得了新的SOTA性能。代码将公开发布。
英文摘要：
Multiple instance learning (MIL) problem is currently solved from either bag-classification or instance-classification perspective, both of which ignore important information contained in some instances and result in limited performance. For example, existing methods often face difficulty in learning hard positive instances. In this paper, we formulate MIL as a semi-supervised instance classification problem, so that all the labeled and unlabeled instances can be fully utilized to train a better classifier. The difficulty in this formulation is that all the labeled instances are negative in MIL, and traditional self-training techniques used in semi-supervised learning tend to degenerate in generating pseudo labels for the unlabeled instances in this scenario. To resolve this problem, we propose a weakly-supervised self-training method, in which we utilize the positive bag labels to construct a global constraint and a local constraint on the pseudo labels to prevent them from degenerating and force the classifier to learn hard positive instances. It is worth noting that easy positive instances are instances are far from the decision boundary in the classification process, while hard positive instances are those close to the decision boundary. Through iterative optimization, the pseudo labels can gradually approach the true labels. Extensive experiments on two MNIST synthetic datasets, five traditional MIL benchmark datasets and two histopathology whole slide image datasets show that our method achieved new SOTA performance on all of them. The code will be publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
