Multi-instance embedding learning with deconfounded instance-level prediction

Abstract

Confounded information is an objective fact when using multi-instance learning (MIL) to classify bags of instances, which may be inherited by MIL embedding methods and lead to questionable bag label prediction. To respond to this problem, we propose the multi-instance embedding learning with deconfounded instance-level prediction algorithm. Unlike traditional embedding-based strategies, we design a deconfounded optimization goal to maximize the distinction between instances in positive and negative bags. In addition, we present and use bag-level embedding with feature distillation to reduce the MIL classification task to a single-instance learning problem. Under the theoretical analysis, the embedding cohesiveness and feature magnitude metrics are developed to explain the benefits of the proposed deconfounded technique in MIL settings. Extensive experiments on thirty-four data sets demonstrate that our proposed method has the best overall performance over other state-of-the-art MIL methods. This strategy, in particular, has a substantial advantage on web data sets. Source codes are available at https://github.com/InkiInki/MEDI .