  When there are unlabeled Out-Of-Distribution (OOD) data from other classes,
Semi-Supervised Learning (SSL) methods suffer from severe performance
degradation and even get worse than merely training on labeled data. In this
paper, we empirically analyze Pseudo-Labeling (PL) in class-mismatched SSL. PL
is a simple and representative SSL method that transforms SSL problems into
supervised learning by creating pseudo-labels for unlabeled data according to
the model's prediction. We aim to answer two main questions: (1) How do OOD
data influence PL? (2) What is the proper usage of OOD data with PL? First, we
show that the major problem of PL is imbalanced pseudo-labels on OOD data.
Second, we find that OOD data can help classify In-Distribution (ID) data given
their OOD ground truth labels. Based on the findings, we propose to improve PL
in class-mismatched SSL with two components -- Re-balanced Pseudo-Labeling
(RPL) and Semantic Exploration Clustering (SEC). RPL re-balances pseudo-labels
of high-confidence data, which simultaneously filters out OOD data and
addresses the imbalance problem. SEC uses balanced clustering on low-confidence
data to create pseudo-labels on extra classes, simulating the process of
training with ground truth. Experiments show that our method achieves steady
improvement over supervised baseline and state-of-the-art performance under all
class mismatch ratios on different benchmarks.
