  Semi-supervised learning (SSL) is the branch of machine learning that aims to
improve learning performance by leveraging unlabeled data when labels are
insufficient. Recently, SSL with deep models has proven to be successful on
standard benchmark tasks. However, they are still vulnerable to various
robustness threats in real-world applications as these benchmarks provide
perfect unlabeled data, while in realistic scenarios, unlabeled data could be
corrupted. Many researchers have pointed out that after exploiting corrupted
unlabeled data, SSL suffers severe performance degradation problems. Thus,
there is an urgent need to develop SSL algorithms that could work robustly with
corrupted unlabeled data. To fully understand robust SSL, we conduct a survey
study. We first clarify a formal definition of robust SSL from the perspective
of machine learning. Then, we classify the robustness threats into three
categories: i) distribution corruption, i.e., unlabeled data distribution is
mismatched with labeled data; ii) feature corruption, i.e., the features of
unlabeled examples are adversarially attacked; and iii) label corruption, i.e.,
the label distribution of unlabeled data is imbalanced. Under this unified
taxonomy, we provide a thorough review and discussion of recent works that
focus on these issues. Finally, we propose possible promising directions within
robust SSL to provide insights for future research.
