Learning from Label Proportion with Online Pseudo-Label Decision by Regret Minimization

Shinnosuke Matsuo, Ryoma Bise, Seiichi Uchida, Daiki Suehiro
2/17/2023
cs.CV

No official code found. Best implementation: matsuo-shinnosuke/online-pseudo-labeling (5 stars, last commit 2025-11-01).

Abstract

This paper proposes a novel and efficient method for Learning from Label Proportions (LLP), whose goal is to train a classifier only by using the class label proportions of instance sets, called bags. We propose a novel LLP method based on an online pseudo-labeling method with regret minimization. As opposed to the previous LLP methods, the proposed method effectively works even if the bag sizes are large. We demonstrate the effectiveness of the proposed method using some benchmark datasets.

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Code Implementations(1)

matsuo-shinnosuke/online-pseudo-labelingCommunity100%

Learning from Label Proportion with Online Pseudo-Label Decision by Regret Minimization in ICASSP2023.

50PythonOct 27, 202211 months ago
learning-from-label-proportionsweakly-supervised-learning

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Cite this paper

@article{matsuo2023learning,
  title  = {Learning from Label Proportion with Online Pseudo-Label Decision by Regret Minimization},
  author = {Shinnosuke Matsuo and Ryoma Bise and Seiichi Uchida and Daiki Suehiro},
  year   = {2023},
  eprint = {2302.08947},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.08947v1}
}

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