Robust Self-Supervised Multi-Instance Learning with Structure Awareness

Abstract

Multi-instance learning (MIL) is a supervised learning where each example is a labeled bag with many instances. The typical MIL strategies are to train an instance-level feature extractor followed by aggregating instances features as bag-level representation with labeled information. However, learning such a bag-level representation highly depends on a large number of labeled datasets, which are difficult to get in real-world scenarios. In this paper, we make the first attempt to propose a robust Self-supervised Multi-Instance LEarning architecture with Structure awareness (SMILES) that learns unsupervised bag representation. Our proposed approach is: 1) permutation invariant to the order of instances in bag; 2) structure-aware to encode the topological structures among the instances; and 3) robust against instances noise or permutation. Specifically, to yield robust MIL model without label information, we augment the multi-instance bag and train the representation encoder to maximize the agreement between the representations of the same bag in its different augmented forms. Moreover, to capture topological structures from nearby instances in bags, our framework learns optimal graph structures for the bags and these graphs are optimized together with message passing layers and ordered weighted averaging operator towards contrastive loss. Our main theorem characterizes the permutation invariance of the bag representation. Compared with state-of-the-art supervised MIL baselines, SMILES achieves average improvement of 4.9%, 4.4% in classification accuracy on 5 benchmark datasets and 20 newsgroups datasets, respectively. In addition, we show that the model is robust to the input corruption.