MixBag: Bag-Level Data Augmentation for Learning from Label Proportions

• URL: http://arxiv.org/abs/2308.08822v1
• Date: Thu, 17 Aug 2023 07:06:50 GMT
• Title: MixBag: Bag-Level Data Augmentation for Learning from Label Proportions
• Authors: Takanori Asanomi, Shinnosuke Matsuo, Daiki Suehiro, Ryoma Bise
• Abstract summary: Learning from label proportions (LLP) is a promising weakly supervised learning problem. We propose a bag-level data augmentation method for LLP called MixBag.
• Score: 4.588028371034407
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Learning from label proportions (LLP) is a promising weakly supervised learning problem. In LLP, a set of instances (bag) has label proportions, but no instance-level labels are given. LLP aims to train an instance-level classifier by using the label proportions of the bag. In this paper, we propose a bag-level data augmentation method for LLP called MixBag, based on the key observation from our preliminary experiments; that the instance-level classification accuracy improves as the number of labeled bags increases even though the total number of instances is fixed. We also propose a confidence interval loss designed based on statistical theory to use the augmented bags effectively. To the best of our knowledge, this is the first attempt to propose bag-level data augmentation for LLP. The advantage of MixBag is that it can be applied to instance-level data augmentation techniques and any LLP method that uses the proportion loss. Experimental results demonstrate this advantage and the effectiveness of our method.

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