In this work, we propose a simple model that provides permutation invariant maximally predictive prototype generator from a given dataset, which leads to interpretability of the solution and concrete insights to the nature and the solution of a problem. Our aim is to find out prototypes in the feature space to map the collection of instances (i.e. bags) to a distance feature space and simultaneously learn a linear classifier for multiple instance learning (MIL). Our experiments on classical MIL benchmark datasets demonstrate that proposed framework is an accurate and efficient classifier compared to the existing approaches.
翻译:在这项工作中,我们提出了一个简单模型,从一个特定数据集中提供变异性、最大预测性原型生成器,从而可以解释解决办法,并具体洞察问题的性质和解决办法。我们的目标是在地物空间中找到原型,将实例(如袋)的收集图绘制成一个远距离地物空间,同时学习线性分类,供多个实例学习(MIL)。我们在传统的MIL基准数据集方面的实验表明,与现有方法相比,拟议的框架是一个准确有效的分类器。