Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning
This addresses classification tasks in domains like medical imaging where data has ambiguous labels, but it is incremental as it builds on existing MIPL methods.
The paper tackles the problem of multi-instance partial-label learning (MIPL), where objects are represented as bags with candidate label sets, by proposing DEMIPL, which uses a disambiguation attention mechanism and momentum-based strategy to embed bags into vectors and identify ground-truth labels, achieving superior performance on benchmark and real-world datasets.
In many real-world tasks, the concerned objects can be represented as a multi-instance bag associated with a candidate label set, which consists of one ground-truth label and several false positive labels. Multi-instance partial-label learning (MIPL) is a learning paradigm to deal with such tasks and has achieved favorable performances. Existing MIPL approach follows the instance-space paradigm by assigning augmented candidate label sets of bags to each instance and aggregating bag-level labels from instance-level labels. However, this scheme may be suboptimal as global bag-level information is ignored and the predicted labels of bags are sensitive to predictions of negative instances. In this paper, we study an alternative scheme where a multi-instance bag is embedded into a single vector representation. Accordingly, an intuitive algorithm named DEMIPL, i.e., Disambiguated attention Embedding for Multi-Instance Partial-Label learning, is proposed. DEMIPL employs a disambiguation attention mechanism to aggregate a multi-instance bag into a single vector representation, followed by a momentum-based disambiguation strategy to identify the ground-truth label from the candidate label set. Furthermore, we introduce a real-world MIPL dataset for colorectal cancer classification. Experimental results on benchmark and real-world datasets validate the superiority of DEMIPL against the compared MIPL and partial-label learning approaches.