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2020 – today
- 2023
[c14]Atsuyuki Miyai, Qing Yu, Daiki Ikami, Go Irie, Kiyoharu Aizawa:
Rethinking Rotation in Self-Supervised Contrastive Learning: Adaptive Positive or Negative Data Augmentation. WACV 2023: 2808-2817- 2022
- [c13]Qing Yu
, Daiki Ikami, Go Irie, Kiyoharu Aizawa:
Self-Labeling Framework for Novel Category Discovery over Domains. AAAI 2022: 3161-3169 - [i9]Atsuyuki Miyai, Qing Yu, Daiki Ikami, Go Irie, Kiyoharu Aizawa:
Rethinking Rotation in Self-Supervised Contrastive Learning: Adaptive Positive or Negative Data Augmentation. CoRR abs/2210.12681 (2022) - 2021
- [c12]Yu Mitsuzumi, Go Irie, Daiki Ikami, Takashi Shibata:
Generalized Domain Adaptation. CVPR 2021: 1084-1093 - [c11]Takashi Shibata, Go Irie, Daiki Ikami, Yu Mitsuzumi:
Learning with Selective Forgetting. IJCAI 2021: 989-996 - [c10]Daiki Ikami, Go Irie, Takashi Shibata:
Constrained Weight Optimization for Learning without Activation Normalization. WACV 2021: 2605-2613 - [i8]Daiki Tanaka, Daiki Ikami, Kiyoharu Aizawa:
A Novel Perspective for Positive-Unlabeled Learning via Noisy Labels. CoRR abs/2103.04685 (2021) - [i7]Yu Mitsuzumi, Go Irie, Daiki Ikami, Takashi Shibata:
Generalized Domain Adaptation. CoRR abs/2106.01656 (2021) - 2020
- [j1]Shota Horiguchi, Daiki Ikami, Kiyoharu Aizawa:
Significance of Softmax-Based Features in Comparison to Distance Metric Learning-Based Features. IEEE Trans. Pattern Anal. Mach. Intell. 42(5): 1279-1285 (2020) - [c9]Go Irie, Daiki Ikami, Takahito Kawanishi, Kunio Kashino:
Cascaded Transposed Long-Range Convolutions for Monocular Depth Estimation. ACCV (3) 2020: 437-453 - [c8]Qing Yu, Daiki Ikami, Go Irie, Kiyoharu Aizawa:
Multi-task Curriculum Framework for Open-Set Semi-supervised Learning. ECCV (12) 2020: 438-454 - [c7]Takumi Kawashima, Qing Yu, Akari Asai, Daiki Ikami, Kiyoharu Aizawa:
The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals. ICPR 2020: 1438-1445 - [i6]Qing Yu, Daiki Ikami, Go Irie, Kiyoharu Aizawa:
Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning. CoRR abs/2007.11330 (2020) - [i5]Takumi Kawashima, Qing Yu, Akari Asai, Daiki Ikami, Kiyoharu Aizawa:
The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals. CoRR abs/2011.01655 (2020)
2010 – 2019
- 2019
- [c6]Akari Asai, Daiki Ikami, Kiyoharu Aizawa:
Multi-Task Learning based on Separable Formulation of Depth Estimation and its Uncertainty. CVPR Workshops 2019: 21-24 - [c5]Koki Tsubota, Daiki Ikami, Kiyoharu Aizawa:
Synthesis of Screentone Patterns of Manga Characters. ISM 2019: 212-215 - 2018
- [c4]Daiki Ikami, Toshihiko Yamasaki, Kiyoharu Aizawa:
Local and Global Optimization Techniques in Graph-Based Clustering. CVPR 2018: 3456-3464 - [c3]Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, Kiyoharu Aizawa:
Joint Optimization Framework for Learning With Noisy Labels. CVPR 2018: 5552-5560 - [c2]Daiki Ikami, Toshihiko Yamasaki, Kiyoharu Aizawa:
Fast and Robust Estimation for Unit-Norm Constrained Linear Fitting Problems. CVPR 2018: 8147-8155 - [i4]Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, Kiyoharu Aizawa:
Joint Optimization Framework for Learning with Noisy Labels. CoRR abs/1803.11364 (2018) - [i3]Akito Takeki, Daiki Ikami, Go Irie, Kiyoharu Aizawa:
Parallel Grid Pooling for Data Augmentation. CoRR abs/1803.11370 (2018) - 2017
- [c1]Daiki Ikami, Toshihiko Yamasaki, Kiyoharu Aizawa:
Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems. CVPR 2017: 7206-7214 - [i2]Daiki Ikami, Toshihiko Yamasaki, Kiyoharu Aizawa:
Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems. CoRR abs/1705.09549 (2017) - [i1]Shota Horiguchi, Daiki Ikami, Kiyoharu Aizawa:
Significance of Softmax-based Features in Comparison to Distance Metric Learning-based Features. CoRR abs/1712.10151 (2017)
Coauthor Index
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