MixBag: Bag-Level Data Augmentation for Learning from Label Proportions
Takanori Asanomi Shinnosuke Matsuo Daiki Suehiro Ryoma Bise
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.