Multi-instance learning is a type of weakly supervised learning. It deals with tasks where the data is a set of bags and each bag is a set of instances. Only the bag labels are observed whereas the labels for the instances are unknown. An important advantage of multi-instance learning is that by representing objects as a bag of instances, it is able to preserve the inherent dependencies among parts of the objects. Unfortunately, most existing algorithms assume all instances to be \textit{identically and independently distributed}, which violates real-world scenarios since the instances within a bag are rarely independent. In this work, we propose the Multi-Instance Variational Auto-Encoder (MIVAE) algorithm which explicitly models the dependencies among the instances for predicting both bag labels and instance labels. Experimental results on several multi-instance benchmarks and end-to-end medical imaging datasets demonstrate that MIVAE performs better than state-of-the-art algorithms for both instance label and bag label prediction tasks.

中文翻译：

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使用变分自动编码器预测实例和包装袋标签的非IID多实例学习

多实例学习是一种弱监督学习。它处理的任务是数据是一组袋子，每个袋子是一组实例。仅观察到袋子的标签，而实例的标签是未知的。多实例学习的一个重要优点是，通过将对象表示为实例包，它可以保留对象各部分之间的固有依赖性。不幸的是，大多数现有算法都假定所有实例都是\ textit {独立且独立地分布}，这违反了现实情况，因为包中的实例很少是独立的。在这项工作中，我们提出了多实例变体自动编码器（MIVAE）算法，该算法显式地对实例之间的依赖关系进行建模，以预测袋子标签和实例标签。