6 citations · 7 across the 5 of their papers we have counts for
7 papers
Generalized Domain Adaptation
Yu Mitsuzumi, Go Irie, Daiki Ikami +1
Many variants of unsupervised domain adaptation (UDA) problems have been proposed and solved individually. Its side effect is that a method that works for one variant is often inef…
A Novel Perspective for Positive-Unlabeled Learning via Noisy Labels
Daiki Tanaka, Daiki Ikami, Kiyoharu Aizawa
Positive-unlabeled learning refers to the process of training a binary classifier using only positive and unlabeled data. Although unlabeled data can contain positive data, all unl…
The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals
Takumi Kawashima, Qing Yu, Akari Asai +2
We propose a new optimization framework for aleatoric uncertainty estimation in regression problems. Existing methods can quantify the error in the target estimation, but they tend…
Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning
Qing Yu, Daiki Ikami, Go Irie +1
Semi-supervised learning (SSL) has been proposed to leverage unlabeled data for training powerful models when only limited labeled data is available. While existing SSL methods ass…
Parallel Grid Pooling for Data Augmentation
Akito Takeki, Daiki Ikami, Go Irie +1
Convolutional neural network (CNN) architectures utilize downsampling layers, which restrict the subsequent layers to learn spatially invariant features while reducing computationa…
Joint Optimization Framework for Learning with Noisy Labels
Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki +1
Deep neural networks (DNNs) trained on large-scale datasets have exhibited significant performance in image classification. Many large-scale datasets are collected from websites, h…