103 citations · 184 across the 9 of their papers we have counts for
9 papers
Rethinking Rotation in Self-Supervised Contrastive Learning: Adaptive Positive or Negative Data Augmentation
Atsuyuki Miyai, Qing Yu, Daiki Ikami +2
Rotation is frequently listed as a candidate for data augmentation in contrastive learning but seldom provides satisfactory improvements. We argue that this is because the rotated…
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…
Significance of Softmax-based Features in Comparison to Distance Metric Learning-based Features
Shota Horiguchi, Daiki Ikami, Kiyoharu Aizawa
The extraction of useful deep features is important for many computer vision tasks. Deep features extracted from classification networks have proved to perform well in those tasks.…
Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems
Daiki Ikami, Toshihiko Yamasaki, Kiyoharu Aizawa
We propose the residual expansion (RE) algorithm: a global (or near-global) optimization method for nonconvex least squares problems. Unlike most existing nonconvex optimization te…