6 citations · 7 across the 4 of their papers we have counts for
5 papers · 1 filter
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…
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…
Computational Attention System for Children, Adults and Elderly
Onkar Krishna, Kiyoharu Aizawa, Go Irie
The existing computational visual attention systems have focused on the objective to basically simulate and understand the concept of visual attention system in adults. Consequentl…
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…