Supervised learningtechniques construct predictive models by learning from a large number of training
examples, where each training example has a label indicating its ground-truth output. Though current
techniques have achieved great success, it is noteworthy that in many tasks it is difficult to get strong
supervision information like fully ground-truth labels due to the high cost of the data-labeling process. Thus,
it is desirable for machine-learning techniques to work with weak supervision. This article reviews some
research progress of weakly supervised learning, focusing on three typical types of weak supervision:
incomplete supervision, where only a subset of training data is given with labels; inexact supervision, where
the training data are given with only coarse-grained labels; and inaccurate supervision, where the given
labels are not always ground-truth
