Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias
International Conference on Artificial Intelligence and Statistics(2023)
摘要
Self-training is a well-known approach for semi-supervised learning. It
consists of iteratively assigning pseudo-labels to unlabeled data for which the
model is confident and treating them as labeled examples. For neural networks,
softmax prediction probabilities are often used as a confidence measure,
although they are known to be overconfident, even for wrong predictions. This
phenomenon is particularly intensified in the presence of sample selection
bias, i.e., when data labeling is subject to some constraint. To address this
issue, we propose a novel confidence measure, called 𝒯-similarity,
built upon the prediction diversity of an ensemble of linear classifiers. We
provide the theoretical analysis of our approach by studying stationary points
and describing the relationship between the diversity of the individual members
and their performance. We empirically demonstrate the benefit of our confidence
measure for three different pseudo-labeling policies on classification datasets
of various data modalities. The code is available at
https://github.com/ambroiseodt/tsim.
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