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The scarcity of labeled data is a critical obstacle to deep learning.Semi-supervised learning (SSL) provides a promising way to leverage unlabeleddata by pseudo labels. However, when the size of labeled data is very small(say a few labeled samples per class), SSL performs poorly and unstably,possibly due to the low quality of learned pseudo labels. In this paper, wepropose a new SSL method called DP-SSL that adopts an innovative dataprogramming (DP) scheme to generate probabilistic labels for unlabeled data.Different from existing DP methods that rely on human experts to provideinitial labeling functions (LFs), we develop a multiple-choice learning~(MCL)based approach to automatically generate LFs from scratch in SSL style. Withthe noisy labels produced by the LFs, we design a label model to resolve theconflict and overlap among the noisy labels, and finally infer probabilisticlabels for unlabeled samples. Extensive experiments on four standard SSLbenchmarks show that DP-SSL can provide reliable labels for unlabeled data andachieve better classification performance on test sets than existing SSLmethods, especially when only a small number of labeled samples are available.Concretely, for CIFAR-10 with only 40 labeled samples, DP-SSL achieves 93.82%annotation accuracy on unlabeled data and 93.46% classification accuracy ontest data, which are higher than the SOTA results.

DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples