Wei-I Lin (r10922076@ntu.edu.tw or empennage98@gmail.com)
This summer (Jun. - Aug. 2022)
Complementary label learning (CLL) is a weak type of multi-class classification problem where the learning algorithms have access to only complementary labels, classes that an instance does not belong to. We aim to understand how humans behave when asked to provide complementary labels in various aspects, including the time to annotate, quality of the annotations, bias in selecting complementary labels, etc.