Towards Semi-supervised Learning with Non-random Missing Labels
Yue Duan Zhen Zhao Lei Qi Luping Zhou Lei Wang Yinghuan Shi
Semi-supervised learning (SSL) tackles the label missing problem by enabling
the effective usage of unlabeled data. While existing SSL methods focus on the
traditional setting, a practical and challenging scenario called label Missing
Not At Random (MNAR) is usually ignored. In MNAR, the labeled and unlabeled
data fall into different class distributions resulting in biased label
imputation, which deteriorates the performance of SSL models. In this work,
class transition tracking based Pseudo-Rectifying Guidance (PRG) is devised for
MNAR. We explore the class-level guidance information obtained by the Markov
random walk, which is modeled on a dynamically created graph built over the
class tracking matrix. PRG unifies the historical information of class
distribution and class transitions caused by the pseudo-rectifying procedure to
maintain the model's unbiased enthusiasm towards assigning pseudo-labels to all
classes, so as the quality of pseudo-labels on both popular classes and rare
classes in MNAR could be improved. Finally, we show the superior performance of
PRG across a variety of MNAR scenarios, outperforming the latest SSL approaches
combining bias removal solutions by a large margin. Code and model weights are
available at https://github.com/NJUyued/PRG4SSL-MNAR.