An adaptive class prototype generation framework for partial label learning

Partial label learning involves instances with multiple potential labels, of which only one is correct. This process poses challenges when managing limited-size datasets and ambiguity arising from overlapping characteristics across various categories. This paper proposes a novel framework based on class prototype generation to address these issues. The framework integrates a generator that creates label-specific prototypes, a discriminator that assigns weights reflecting the representativeness of examples, and a classifier learning with the assistance of the generator and discriminator. We introduce an adaptive loss adjustment method intended to dynamically balance each model’s weights and mitigate the potential negative impact of inferior examples. Experimental results demonstrate our method’s superiority over state-of-the-art techniques on widely used real-world datasets, with various visualizations illustrating its effectiveness.

Authors

• Haixiang Li
• Min Fang
• Xiao Li Nanjing University
• Bo Chen Middle Tennessee State University

Keywords

• Partial label learning
• Generative adversarial networks
• Prototype generation