Pseudo-Representation Labeling Semi-Supervised Learning

• URL: http://arxiv.org/abs/2006.00429v1
• Date: Sun, 31 May 2020 03:55:41 GMT
• Title: Pseudo-Representation Labeling Semi-Supervised Learning
• Authors: Song-Bo Yang, Tian-li Yu
• Abstract summary: In recent years, semi-supervised learning has shown tremendous success in leveraging unlabeled data to improve the performance of deep learning models. 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. Compared with the existing approaches, the pseudo-representation labeling is more intuitive and can effectively solve practical problems in the real world.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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