Something for (almost) nothing: Improving deep ensemble calibration using unlabeled data

Konstantinos Pitas; Julyan Arbel

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arXiv · 2310.02885

Something for (almost) nothing: Improving deep ensemble calibration using unlabeled data

Konstantinos Pitas·Julyan Arbel

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Abstract

We present a method to improve the calibration of deep ensembles in the small training data regime in the presence of unlabeled data. Our approach is extremely simple to implement: given an unlabeled set, for each unlabeled data point, we simply fit a different randomly selected label with each ensemble member. We provide a theoretical analysis based on a PAC-Bayes bound which guarantees that if we fit such a labeling on unlabeled data, and the true labels on the training data, we obtain low negative log-likelihood and high ensemble diversity on testing samples. Empirically, through detailed experiments, we find that for low to moderately-sized training sets, our ensembles are more diverse and provide better calibration than standard ensembles, sometimes significantly.

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BibTeX RIS

Konstantinos Pitas, Julyan Arbel. 2023-10-04. Something for (almost) nothing: Improving deep ensemble calibration using unlabeled data. https://arxiv.org/abs/2310.02885

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