Counting Network for Learning from Majority Label
Kaito Shiku Shinnosuke Matsuo Daiki Suehiro Ryoma Bise
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 is assigned as the bag's label. LML aims to classify
instances using bag-level majority classes. This problem is valuable in various
applications. Existing MIL methods are unsuitable for LML due to aggregating
confidences, which may lead to inconsistency between the bag-level label and
the label obtained by counting the number of instances for each class. This may
lead to incorrect instance-level classification. We propose a counting network
trained to produce the bag-level majority labels estimated by counting the
number of instances for each class. This led to the consistency of the majority
class between the network outputs and one obtained by counting the number of
instances. Experimental results show that our counting network outperforms
conventional MIL methods on four datasets The code is publicly available at
https://github.com/Shiku-Kaito/Counting-Network-for-Learning-from-Majority-Label.