Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning

This article likely presents a novel loss function designed to improve the performance of machine learning models in scenarios where labels are incomplete or ambiguous. The focus is on multi-instance learning, a setting where labels are assigned to sets of instances rather than individual ones. The term "calibratable" suggests the loss function aims to provide reliable probability estimates, which is crucial for practical applications. The source being ArXiv indicates this is a research paper, likely detailing the mathematical formulation, experimental results, and comparisons to existing methods.