  Although data is abundant, data labeling is expensive. Semi-supervised
learning methods combine a few labeled samples with a large corpus of unlabeled
data to effectively train models. This paper introduces our proposed method
LiDAM, a semi-supervised learning approach rooted in both domain adaptation and
self-paced learning. LiDAM first performs localized domain shifts to extract
better domain-invariant features for the model that results in more accurate
clusters and pseudo-labels. These pseudo-labels are then aligned with real
class labels in a self-paced fashion using a novel iterative matching technique
that is based on majority consistency over high-confidence predictions.
Simultaneously, a final classifier is trained to predict ground-truth labels
until convergence. LiDAM achieves state-of-the-art performance on the CIFAR-100
dataset, outperforming FixMatch (73.50% vs. 71.82%) when using 2500 labels.
