2 citations · 4 across the 16 of their papers we have counts for
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cs.CV2023
MixBag: Bag-Level Data Augmentation for Learning from Label Proportions
Takanori Asanomi, Shinnosuke Matsuo, Daiki Suehiro +1
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 gi…
cs.CV2023
Learning from Label Proportion with Online Pseudo-Label Decision by Regret Minimization
Shinnosuke Matsuo, Ryoma Bise, Seiichi Uchida +1
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 instanc…