Improving realistic semi-supervised learning with doubly robust estimation

• URL: http://arxiv.org/abs/2502.00279v1
• Date: Sat, 01 Feb 2025 02:34:12 GMT
• Title: Improving realistic semi-supervised learning with doubly robust estimation
• Authors: Khiem Pham, Charles Herrmann, Ramin Zabih,
• Abstract summary: A major challenge in Semi-Supervised Learning (SSL) is the limited information available about the class distribution in the unlabeled data. We propose to explicitly estimate the unlabeled class distribution, which is a finite-dimensional parameter, emphas an initial step, using a doubly robust estimator with a strong theoretical guarantee. This estimate can then be integrated into existing methods to pseudo-label the unlabeled data during training more accurately.
• Score: 8.828699635463265
• License:
• Abstract: A major challenge in Semi-Supervised Learning (SSL) is the limited information available about the class distribution in the unlabeled data. In many real-world applications this arises from the prevalence of long-tailed distributions, where the standard pseudo-label approach to SSL is biased towards the labeled class distribution and thus performs poorly on unlabeled data. Existing methods typically assume that the unlabeled class distribution is either known a priori, which is unrealistic in most situations, or estimate it on-the-fly using the pseudo-labels themselves. We propose to explicitly estimate the unlabeled class distribution, which is a finite-dimensional parameter, \emph{as an initial step}, using a doubly robust estimator with a strong theoretical guarantee; this estimate can then be integrated into existing methods to pseudo-label the unlabeled data during training more accurately. Experimental results demonstrate that incorporating our techniques into common pseudo-labeling approaches improves their performance.

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