  In recent years, semi-supervised learning (SSL) has shown tremendous success
in leveraging unlabeled data to improve the performance of deep learning
models, which significantly reduces the demand for large amounts of labeled
data. Many SSL techniques have been proposed and have shown promising
performance on famous datasets such as ImageNet and CIFAR-10. However, some
exiting techniques (especially data augmentation based) are not suitable for
industrial applications empirically. Therefore, this work proposes the
pseudo-representation labeling, a simple and flexible framework that utilizes
pseudo-labeling techniques to iteratively label a small amount of unlabeled
data and use them as training data. In addition, our framework is integrated
with self-supervised representation learning such that the classifier gains
benefits from representation learning of both labeled and unlabeled data. This
framework can be implemented without being limited at the specific model
structure, but a general technique to improve the existing model. Compared with
the existing approaches, the pseudo-representation labeling is more intuitive
and can effectively solve practical problems in the real world. Empirically, it
outperforms the current state-of-the-art semi-supervised learning methods in
industrial types of classification problems such as the WM-811K wafer map and
the MIT-BIH Arrhythmia dataset.
