A Fusion Technique of Schema and Syntax Rules for Validating Open Data. - Shin'ya Yamaguchi; Kimio Kuramitsu (ACIIDS (Extended Posters), 2017)

Multiple Pretext-Task for Self-Supervised Learning via Mixing Multiple Image Transformations. - Shin'ya Yamaguchi; Sekitoshi Kanai; Tetsuya Shioda; Shoichiro Takeda (CoRR, 2019)

Effective Data Augmentation with Multi-Domain Learning GANs. - Shin'ya Yamaguchi; Sekitoshi Kanai; Takeharu Eda (AAAI, 2020)

F-Drop&Match: GANs with a Dead Zone in the High-Frequency Domain. - Shin'ya Yamaguchi; Sekitoshi Kanai (ICCV, 2021)

Image Enhanced Rotation Prediction for Self-Supervised Learning. - Shin'ya Yamaguchi; Sekitoshi Kanai; Tetsuya Shioda; Shoichiro Takeda (ICIP, 2021)

Constraining Logits by Bounded Function for Adversarial Robustness. - Sekitoshi Kanai; Masanori Yamada; Shin'ya Yamaguchi; Hiroshi Takahashi; Yasutoshi Ida (IJCNN, 2021)

Pruning Randomly Initialized Neural Networks with Iterative Randomization. - Daiki Chijiwa; Shin'ya Yamaguchi; Yasutoshi Ida; Kenji Umakoshi; Tomohiro Inoue (NeurIPS, 2021)

Learning Robust Convolutional Neural Networks with Relevant Feature Focusing Via Explanations. - Kazuki Adachi; Shin'ya Yamaguchi (ICME, 2022)

Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks. - Daiki Chijiwa; Shin'ya Yamaguchi; Atsutoshi Kumagai; Yasutoshi Ida (NeurIPS, 2022)

Covariance-aware Feature Alignment with Pre-computed Source Statistics for Test-time Adaptation. - Kazuki Adachi; Shin'ya Yamaguchi; Atsutoshi Kumagai (CoRR, 2022)

Switching One-Versus-the-Rest Loss to Increase the Margin of Logits for Adversarial Robustness. - Sekitoshi Kanai; Shin'ya Yamaguchi; Masanori Yamada; Hiroshi Takahashi; Yasutoshi Ida (CoRR, 2022)

Generative Semi-supervised Learning with Meta-Optimized Synthetic Samples. - Shin'ya Yamaguchi (ACML, 2023)

Adversarial Finetuning with Latent Representation Constraint to Mitigate Accuracy-Robustness Tradeoff. - Satoshi Suzuki; Shin'ya Yamaguchi; Shoichiro Takeda; Sekitoshi Kanai; Naoki Makishima; Atsushi Ando; Ryo Masumura (ICCV, 2023)

Covariance-Aware Feature Alignment with Pre-Computed Source Statistics for Test-Time Adaptation to Multiple Image Corruptions. - Kazuki Adachi; Shin'ya Yamaguchi; Atsutoshi Kumagai (ICIP, 2023)

One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training. - Sekitoshi Kanai; Shin'ya Yamaguchi; Masanori Yamada; Hiroshi Takahashi; Kentaro Ohno; Yasutoshi Ida (ICML, 2023)

Regularizing Neural Networks with Meta-Learning Generative Models. - Shin'ya Yamaguchi; Daiki Chijiwa; Sekitoshi Kanai; Atsutoshi Kumagai; Hisashi Kashima (NeurIPS, 2023)

Revisiting Permutation Symmetry for Merging Models between Different Datasets. - Masanori Yamada; Tomoya Yamashita; Shin'ya Yamaguchi; Daiki Chijiwa (CoRR, 2023)

On the Limitation of Diffusion Models for Synthesizing Training Datasets. - Shin'ya Yamaguchi; Takuma Fukuda (CoRR, 2023)

Toward Data Efficient Model Merging between Different Datasets without Performance Degradation. - Masanori Yamada; Tomoya Yamashita; Shin'ya Yamaguchi; Daiki Chijiwa (ACML, 2024)

Analyzing Diffusion Models on Synthesizing Training Datasets. - Shin'ya Yamaguchi (ACML, 2024)

Adaptive Random Feature Regularization on Fine-tuning Deep Neural Networks. - Shin'ya Yamaguchi; Sekitoshi Kanai; Kazuki Adachi; Daiki Chijiwa (CVPR, 2024)

Test-time Similarity Modification for Person Re-identification toward Temporal Distribution Shift. - Kazuki Adachi; Shohei Enomoto; Taku Sasaki; Shin'ya Yamaguchi (IJCNN, 2024)

Test-time Adaptation Meets Image Enhancement: Improving Accuracy via Uncertainty-aware Logit Switching. - Shohei Enomoto; Naoya Hasegawa; Kazuki Adachi; Taku Sasaki; Shin'ya Yamaguchi; Satoshi Suzuki; Takeharu Eda (IJCNN, 2024)

Key Factors Determining the Required Number of Training Images in Person Re-Identification. - Taku Sasaki; Adam S. Walmsley; Kazuki Adachi; Shohei Enomoto; Shin'ya Yamaguchi (IEEE Access, 2024)

Evaluating Time-Series Training Dataset through Lens of Spectrum in Deep State Space Models. - Sekitoshi Kanai; Yasutoshi Ida; Kazuki Adachi; Mihiro Uchida; Tsukasa Yoshida; Shin'ya Yamaguchi (CoRR, 2024)

Explanation Bottleneck Models. - Shin'ya Yamaguchi; Kosuke Nishida (AAAI, 2025)

Post-pre-training for Modality Alignment in Vision-Language Foundation Models. - Shin'ya Yamaguchi; Dewei Feng; Sekitoshi Kanai; Kazuki Adachi; Daiki Chijiwa (CVPR, 2025)

Test-time Adaptation for Regression by Subspace Alignment. - Kazuki Adachi; Shin'ya Yamaguchi; Atsutoshi Kumagai; Tomoki Hamagami (ICLR, 2025)

Zero-shot Concept Bottleneck Models. - Shin'ya Yamaguchi; Kosuke Nishida; Daiki Chijiwa; Yasutoshi Ida (CoRR, 2025)

Transfer learning with pre-trained conditional generative models. - Shin'ya Yamaguchi; Sekitoshi Kanai; Atsutoshi Kumagai; Daiki Chijiwa; Hisashi Kashima (Mach. Learn., 2025)

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption. - Kazuki Adachi; Shin'ya Yamaguchi; Tomoki Hamagami (CoRR, 2025)

Rationale-Enhanced Decoding for Multi-modal Chain-of-Thought. - Shin'ya Yamaguchi; Kosuke Nishida; Daiki Chijiwa (CoRR, 2025)