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In many real-world tasks, the concerned objects can be represented as amulti-instance bag associated with a candidate label set, which consists of oneground-truth label and several false positive labels. Multi-instancepartial-label learning (MIPL) is a learning paradigm to deal with such tasksand has achieved favorable performances. Existing MIPL approach follows theinstance-space paradigm by assigning augmented candidate label sets of bags toeach instance and aggregating bag-level labels from instance-level labels.However, this scheme may be suboptimal as global bag-level information isignored and the predicted labels of bags are sensitive to predictions ofnegative instances. In this paper, we study an alternative scheme where amulti-instance bag is embedded into a single vector representation.Accordingly, an intuitive algorithm named DEMIPL, i.e., Disambiguated attentionEmbedding for Multi-Instance Partial-Label learning, is proposed. DEMIPLemploys a disambiguation attention mechanism to aggregate a multi-instance baginto a single vector representation, followed by a momentum-baseddisambiguation strategy to identify the ground-truth label from the candidatelabel set. Furthermore, we introduce a real-world MIPL dataset for colorectalcancer classification. Experimental results on benchmark and real-worlddatasets validate the superiority of DEMIPL against the compared MIPL andpartial-label learning approaches.

Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning