Learning with Proper Partial Labels

Understand

Partial-label learning is a kind of weakly-supervised learning with inexact labels, where for each training example, we are given a set of candidate labels instead of only one true label.

• Recently, various approaches on partial-label learning have been proposed under different generation models of candidate label sets.
• However, these methods require relatively strong distributional assumptions on the generation models.
• When the assumptions do not hold, the performance of the methods is not guaranteed theoretically.