Daiki Ikami - RankMe

24 papers A* 7A 2B 1C 1Journal 10Unranked 3

Year	Rank	Type	Title / Venue / Authors
2023	A	`conf`	Rethinking Rotation in Self-Supervised Contrastive Learning: Adaptive Positive or Negative Data Augmentation. WACV Atsuyuki Miyai, Qing Yu, Daiki Ikami, Go Irie, Kiyoharu Aizawa
2022	J	`jnl`	Rethinking Rotation in Self-Supervised Contrastive Learning: Adaptive Positive or Negative Data Augmentation. CoRR Atsuyuki Miyai, Qing Yu, Daiki Ikami, Go Irie, Kiyoharu Aizawa
2022	A*	`conf`	Self-Labeling Framework for Novel Category Discovery over Domains. AAAI Qing Yu, Daiki Ikami, Go Irie, Kiyoharu Aizawa
2021	J	`jnl`	A Novel Perspective for Positive-Unlabeled Learning via Noisy Labels. CoRR Daiki Tanaka, Daiki Ikami, Kiyoharu Aizawa
2021	A	`conf`	Constrained Weight Optimization for Learning without Activation Normalization. WACV Daiki Ikami, Go Irie, Takashi Shibata
2021	A*	`conf`	Generalized Domain Adaptation. CVPR Yu Mitsuzumi, Go Irie, Daiki Ikami, Takashi Shibata
2021	J	`jnl`	Generalized Domain Adaptation. CoRR Yu Mitsuzumi, Go Irie, Daiki Ikami, Takashi Shibata
2021	A*	`conf`	Learning with Selective Forgetting. IJCAI Takashi Shibata, Go Irie, Daiki Ikami, Yu Mitsuzumi
2020	—	`conf`	Cascaded Transposed Long-Range Convolutions for Monocular Depth Estimation. ACCV (3) Go Irie, Daiki Ikami, Takahito Kawanishi, Kunio Kashino
2020	J	`jnl`	Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning. CoRR Qing Yu, Daiki Ikami, Go Irie, Kiyoharu Aizawa
2020	—	`conf`	Multi-task Curriculum Framework for Open-Set Semi-supervised Learning. ECCV (12) Qing Yu, Daiki Ikami, Go Irie, Kiyoharu Aizawa
2020	J	`jnl`	Significance of Softmax-Based Features in Comparison to Distance Metric Learning-Based Features. IEEE Trans. Pattern Anal. Mach. Intell. Shota Horiguchi, Daiki Ikami, Kiyoharu Aizawa
2020	B	`conf`	The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals. ICPR Takumi Kawashima, Qing Yu, Akari Asai, Daiki Ikami, Kiyoharu Aizawa
2020	J	`jnl`	The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals. CoRR Takumi Kawashima, Qing Yu, Akari Asai, Daiki Ikami, Kiyoharu Aizawa
2019	—	`conf`	Multi-Task Learning based on Separable Formulation of Depth Estimation and its Uncertainty. CVPR Workshops Akari Asai, Daiki Ikami, Kiyoharu Aizawa
2019	C	`conf`	Synthesis of Screentone Patterns of Manga Characters. ISM Koki Tsubota, Daiki Ikami, Kiyoharu Aizawa
2018	A*	`conf`	Fast and Robust Estimation for Unit-Norm Constrained Linear Fitting Problems. CVPR Daiki Ikami, Toshihiko Yamasaki, Kiyoharu Aizawa
2018	A*	`conf`	Joint Optimization Framework for Learning With Noisy Labels. CVPR Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, Kiyoharu Aizawa
2018	J	`jnl`	Joint Optimization Framework for Learning with Noisy Labels. CoRR Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, Kiyoharu Aizawa
2018	A*	`conf`	Local and Global Optimization Techniques in Graph-Based Clustering. CVPR Daiki Ikami, Toshihiko Yamasaki, Kiyoharu Aizawa
2018	J	`jnl`	Parallel Grid Pooling for Data Augmentation. CoRR Akito Takeki, Daiki Ikami, Go Irie, Kiyoharu Aizawa
2017	A*	`conf`	Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems. CVPR Daiki Ikami, Toshihiko Yamasaki, Kiyoharu Aizawa
2017	J	`jnl`	Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems. CoRR Daiki Ikami, Toshihiko Yamasaki, Kiyoharu Aizawa
2017	J	`jnl`	Significance of Softmax-based Features in Comparison to Distance Metric Learning-based Features. CoRR Shota Horiguchi, Daiki Ikami, Kiyoharu Aizawa