- Title
- Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning
- Creator
- Tang, Wei; Zhang, Weijia; Zhang, Min-Ling
- Relation
- 37th Conference on Neural Information Processing Systems (NeurIPS). Proeedings of the 37th Conference on Neural Information Processing Systems (NeurIPS) (New Orleans, LA 10-16 December, 2023) p. 1-16
- Publisher Link
- http://dx.doi.org/10.48550/arxiv.2305.16912
- Publisher
- Cornell University
- Resource Type
- conference paper
- Date
- 2023
- Description
- 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.
- Subject
- embeddings; multi-instance partial-label learning (MIPL); learning approach; real-world datasets; SDG 3; Sustainable Development Goal
- Identifier
- http://hdl.handle.net/1959.13/1512615
- Identifier
- uon:56638
- Language
- eng
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