Robust Semi-supervised Learning via $f$-Divergence and $α$-Rényi Divergence

• URL: http://arxiv.org/abs/2405.00454v1
• Date: Wed, 1 May 2024 11:16:02 GMT
• Title: Robust Semi-supervised Learning via $f$-Divergence and $α$-Rényi Divergence
• Authors: Gholamali Aminian, Amirhossien Bagheri, Mahyar JafariNodeh, Radmehr Karimian, Mohammad-Hossein Yassaee,
• Abstract summary: This paper investigates a range of empirical risk functions and regularization methods suitable for self-training methods in semi-supervised learning. Inspired by the theoretical foundations rooted in divergences, i.e., $f$-divergences and $alpha$-R'enyi divergence, we also provide valuable insights to enhance the understanding of our empirical risk functions and regularization techniques.
• Score: 2.9965913883475137
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper investigates a range of empirical risk functions and regularization methods suitable for self-training methods in semi-supervised learning. These approaches draw inspiration from various divergence measures, such as $f$-divergences and $\alpha$-R\'enyi divergences. Inspired by the theoretical foundations rooted in divergences, i.e., $f$-divergences and $\alpha$-R\'enyi divergence, we also provide valuable insights to enhance the understanding of our empirical risk functions and regularization techniques. In the pseudo-labeling and entropy minimization techniques as self-training methods for effective semi-supervised learning, the self-training process has some inherent mismatch between the true label and pseudo-label (noisy pseudo-labels) and some of our empirical risk functions are robust, concerning noisy pseudo-labels. Under some conditions, our empirical risk functions demonstrate better performance when compared to traditional self-training methods.

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