In learning from label proportions (LLP), the instances are grouped into bags, and the task is to learn an instance classifier given relative class proportions in training bags. LLP is useful when obtaining individual instance labels is impossible or costly. In this work, we focus on the case of small bags, which allows to design an algorithm that explicitly considers all consistent instance label combinations. In particular, we propose an EM algorithm alternating between optimizing a general neural network instance classifier and incorporating bag-level annotations. Using two different image datasets, we experimentally compare this method with an approach based on normal approximation and two existing LLP methods. The results show that our approach converges faster to a comparable or better solution.
翻译:在从标签比例(LLP)中学习时,这些实例被归为包包,任务是在培训袋中学习一个按相对等级比例分类的例分类器。 LLP在获得单个实例标签时有用,不可能或费用高昂。在这项工作中,我们侧重于小袋的情况,这样就可以设计一种明确考虑所有一致实例标签组合的算法。特别是,我们建议一种EM算法,在优化普通神经网络分类器和纳入包包级注释之间交替使用。我们用两个不同的图像数据集,实验性地将这种方法与基于正常近似和两种现有LLP方法的方法进行比较。结果显示,我们的方法会更快地聚合到一个可比或更好的解决方案。