5 papers
Theoretical Proportion Label Perturbation for Learning from Label Proportions in Large Bags
Shunsuke Kubo, Shinnosuke Matsuo, Daiki Suehiro +4
Learning from label proportions (LLP) is a kind of weakly supervised learning that trains an instance-level classifier from label proportions of bags, which consist of sets of inst…
Counting Network for Learning from Majority Label
Kaito Shiku, Shinnosuke Matsuo, Daiki Suehiro +1
The paper proposes a novel problem in multi-class Multiple-Instance Learning (MIL) called Learning from the Majority Label (LML). In LML, the majority class of instances in a bag i…
Deep Attentive Time Warping
Shinnosuke Matsuo, Xiaomeng Wu, Gantugs Atarsaikhan +4
Similarity measures for time series are important problems for time series classification. To handle the nonlinear time distortions, Dynamic Time Warping (DTW) has been widely used…
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…
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…