Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning

• URL: http://arxiv.org/abs/2305.16912v2
• Date: Thu, 28 Sep 2023 03:29:37 GMT
• Title: Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning
• Authors: Wei Tang, Weijia Zhang, Min-Ling Zhang
• Abstract summary: In many real-world tasks, the concerned objects can be represented as a multi-instance bag associated with a candidate label set. 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. We propose an intuitive algorithm named DEMIPL, i.e., Disambiguated attention Embedding for Multi-Instance Partial-Label learning.
• Score: 68.56193228008466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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