  Pseudo-labeling (PL) and Data Augmentation-based Consistency Training (DACT)
are two approaches widely used in Semi-Supervised Learning (SSL) methods. These
methods exhibit great power in many machine learning tasks by utilizing
unlabeled data for efficient training. But in a more realistic setting (termed
as open-set SSL), where unlabeled dataset contains out-of-distribution (OOD)
samples, the traditional SSL methods suffer severe performance degradation.
Recent approaches mitigate the negative influence of OOD samples by filtering
them out from the unlabeled data. However, it is not clear whether directly
removing the OOD samples is the best choice. Furthermore, why PL and DACT could
perform differently in open-set SSL remains a mystery. In this paper, we
thoroughly analyze various SSL methods (PL and DACT) on open-set SSL and
discuss pros and cons of these two approaches separately. Based on our
analysis, we propose Style Disturbance to improve traditional SSL methods on
open-set SSL and experimentally show our approach can achieve state-of-the-art
results on various datasets by utilizing OOD samples properly. We believe our
study can bring new insights for SSL research.
