4 citations · 7 across the 15 of their papers we have counts for
6 papers · 1 filter
Bounding the Worst-class Error: A Boosting Approach
Yuya Saito, Shinnosuke Matsuo, Seiichi Uchida +1
This paper tackles the problem of the worst-class error rate, instead of the standard error rate averaged over all classes. For example, a three-class classification task with clas…
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…
Functional Knowledge Transfer with Self-supervised Representation Learning
Prakash Chandra Chhipa, Muskaan Chopra, Gopal Mengi +7
This work investigates the unexplored usability of self-supervised representation learning in the direction of functional knowledge transfer. In this work, functional knowledge tra…
Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification
Shota Harada, Ryoma Bise, Kengo Araki +7
Semi-supervised domain adaptation is a technique to build a classifier for a target domain by modifying a classifier in another (source) domain using many unlabeled samples and a s…
Disease Severity Regression with Continuous Data Augmentation
Shumpei Takezaki, Kiyohito Tanaka, Seiichi Uchida +1
Disease severity regression by a convolutional neural network (CNN) for medical images requires a sufficient number of image samples labeled with severity levels. Conditional gener…
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…