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Workshop: 4th Workshop on Self-Supervised Learning: Theory and Practice

How does semi-supervised learning with pseudo-labelers work? A case study

Yiwen Kou · Zixiang Chen · Yuan Cao · Quanquan Gu


Semi-supervised learning is a popular machine learning paradigm that utilizes a large amount of unlabeled data as well as a small amount of labeled data to facilitate learning tasks. While semi-supervised learning has achieved great success in training neural networks, its theoretical understanding remains largely open. In this paper, we aim to theoretically understand a semi-supervised learning approach based on pre-training and linear probing. We prove that, under a certain data generation model and two-layer convolutional neural network, the semi-supervised learning approach can achieve nearly zero test loss, while a neural network directly trained by supervised learning on the same amount of labeled data can only achieve constant test loss. Through this case study, we demonstrate a separation between semi-supervised learning and supervised learning in terms of test loss provided the same amount of labeled data.

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