Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems. - Daiki Ikami; Toshihiko Yamasaki; Kiyoharu Aizawa (CVPR, 2017)

Local and Global Optimization Techniques in Graph-Based Clustering. - Daiki Ikami; Toshihiko Yamasaki; Kiyoharu Aizawa (CVPR, 2018)

Fast and Robust Estimation for Unit-Norm Constrained Linear Fitting Problems. - Daiki Ikami; Toshihiko Yamasaki; Kiyoharu Aizawa (CVPR, 2018)

Joint Optimization Framework for Learning With Noisy Labels. - Daiki Tanaka; Daiki Ikami; Toshihiko Yamasaki; Kiyoharu Aizawa (CVPR, 2018)

Parallel Grid Pooling for Data Augmentation. - Akito Takeki; Daiki Ikami; Go Irie; Kiyoharu Aizawa (CoRR, 2018)

Multi-Task Learning based on Separable Formulation of Depth Estimation and its Uncertainty. - Akari Asai; Daiki Ikami; Kiyoharu Aizawa (CVPR Workshops, 2019)

Synthesis of Screentone Patterns of Manga Characters. - Koki Tsubota; Daiki Ikami; Kiyoharu Aizawa (ISM, 2019)

Cascaded Transposed Long-Range Convolutions for Monocular Depth Estimation. - Go Irie; Daiki Ikami; Takahito Kawanishi; Kunio Kashino (ACCV (3), 2020)

Multi-task Curriculum Framework for Open-Set Semi-supervised Learning. - Qing Yu; Daiki Ikami; Go Irie; Kiyoharu Aizawa (ECCV (12), 2020)

The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals. - Takumi Kawashima; Qing Yu; Akari Asai; Daiki Ikami; Kiyoharu Aizawa (ICPR, 2020)

Significance of Softmax-Based Features in Comparison to Distance Metric Learning-Based Features. - Shota Horiguchi; Daiki Ikami; Kiyoharu Aizawa (IEEE Trans. Pattern Anal. Mach. Intell., 2020)

Generalized Domain Adaptation. - Yu Mitsuzumi; Go Irie; Daiki Ikami; Takashi Shibata 001 (CVPR, 2021)

Learning with Selective Forgetting. - Takashi Shibata 001; Go Irie; Daiki Ikami; Yu Mitsuzumi (IJCAI, 2021)

Constrained Weight Optimization for Learning without Activation Normalization. - Daiki Ikami; Go Irie; Takashi Shibata 001 (WACV, 2021)

A Novel Perspective for Positive-Unlabeled Learning via Noisy Labels. - Daiki Tanaka; Daiki Ikami; Kiyoharu Aizawa (CoRR, 2021)

Self-Labeling Framework for Novel Category Discovery over Domains. - Qing Yu; Daiki Ikami; Go Irie; Kiyoharu Aizawa (AAAI, 2022)

Rethinking Rotation in Self-Supervised Contrastive Learning: Adaptive Positive or Negative Data Augmentation. - Atsuyuki Miyai; Qing Yu; Daiki Ikami; Go Irie; Kiyoharu Aizawa (WACV, 2023)