Top-K Pseudo Labeling for Semi-Supervised Image Classification

Yi Jiang and Hui Sun
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Yi Jiang: Harbin University of Science and Technology, China
Hui Sun: Harbin University of Science and Technology, China

International Journal of Data Warehousing and Mining (IJDWM), 2023, vol. 19, issue 2, 1-18

Abstract: In this paper, a top-k pseudo labeling method for semi-supervised self-learning is proposed. Pseudo labeling is a key technology in semi-supervised self-learning. Briefly, the quality of the pseudo label generated largely determined the convergence of the neural network and the accuracy obtained. In this paper, the authors use a method called top-k pseudo labeling to generate pseudo label during the training of semi-supervised neural network model. The proposed labeling method helps a lot in learning features from unlabeled data. The proposed method is easy to implement and only relies on the neural network prediction and hyper-parameter k. The experiment results show that the proposed method works well with semi-supervised learning on CIFAR-10 and CIFAR-100 datasets. Also, a variant of top-k labeling for supervised learning named top-k regulation is proposed. The experiment results show that various models can achieve higher accuracy on test set when trained with top-k regulation.

Date: 2023
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