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ORCIDShin'ya Yamaguchi
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Conference and Workshop Papers
- 2023
[c13]Shin'ya Yamaguchi:
Generative Semi-supervised Learning with Meta-Optimized Synthetic Samples. ACML 2023: 1510-1525- [c12]Satoshi Suzuki, Shin'ya Yamaguchi, Shoichiro Takeda, Sekitoshi Kanai, Naoki Makishima, Atsushi Ando, Ryo Masumura:
Adversarial Finetuning with Latent Representation Constraint to Mitigate Accuracy-Robustness Tradeoff. ICCV 2023: 4367-4378 - [c11]Kazuki Adachi, Shin'ya Yamaguchi, Atsutoshi Kumagai:
Covariance-Aware Feature Alignment with Pre-Computed Source Statistics for Test-Time Adaptation to Multiple Image Corruptions. ICIP 2023: 800-804 - [c10]Sekitoshi Kanai, Shin'ya Yamaguchi, Masanori Yamada, Hiroshi Takahashi, Kentaro Ohno, Yasutoshi Ida:
One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training. ICML 2023: 15669-15695 - [c9]Shin'ya Yamaguchi, Daiki Chijiwa, Sekitoshi Kanai, Atsutoshi Kumagai, Hisashi Kashima:
Regularizing Neural Networks with Meta-Learning Generative Models. NeurIPS 2023 - 2022
- [c8]Kazuki Adachi, Shin'ya Yamaguchi:
Learning Robust Convolutional Neural Networks with Relevant Feature Focusing Via Explanations. ICME 2022: 1-6 - [c7]Daiki Chijiwa, Shin'ya Yamaguchi, Atsutoshi Kumagai, Yasutoshi Ida:
Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks. NeurIPS 2022 - 2021
- [c6]Shin'ya Yamaguchi, Sekitoshi Kanai:
F-Drop&Match: GANs with a Dead Zone in the High-Frequency Domain. ICCV 2021: 6723-6731 - [c5]Shin'ya Yamaguchi, Sekitoshi Kanai, Tetsuya Shioda, Shoichiro Takeda:
Image Enhanced Rotation Prediction for Self-Supervised Learning. ICIP 2021: 489-493 - [c4]Sekitoshi Kanai, Masanori Yamada, Shin'ya Yamaguchi, Hiroshi Takahashi, Yasutoshi Ida:
Constraining Logits by Bounded Function for Adversarial Robustness. IJCNN 2021: 1-8 - [c3]Daiki Chijiwa, Shin'ya Yamaguchi, Yasutoshi Ida, Kenji Umakoshi, Tomohiro Inoue:
Pruning Randomly Initialized Neural Networks with Iterative Randomization. NeurIPS 2021: 4503-4513 - 2020
- [c2]Shin'ya Yamaguchi, Sekitoshi Kanai, Takeharu Eda:
Effective Data Augmentation with Multi-Domain Learning GANs. AAAI 2020: 6566-6574 - 2017
- [c1]Shin'ya Yamaguchi, Kimio Kuramitsu:
A Fusion Technique of Schema and Syntax Rules for Validating Open Data. ACIIDS (Extended Posters) 2017: 431-442
Informal and Other Publications
- 2024
- [i18]Shin'ya Yamaguchi, Sekitoshi Kanai, Kazuki Adachi, Daiki Chijiwa:
Adaptive Random Feature Regularization on Fine-tuning Deep Neural Networks. CoRR abs/2403.10097 (2024) - [i17]Kazuki Adachi, Shohei Enomoto, Taku Sasaki, Shin'ya Yamaguchi:
Test-time Similarity Modification for Person Re-identification toward Temporal Distribution Shift. CoRR abs/2403.14114 (2024) - [i16]Shohei Enomoto, Naoya Hasegawa, Kazuki Adachi, Taku Sasaki, Shin'ya Yamaguchi, Satoshi Suzuki, Takeharu Eda:
Test-time Adaptation Meets Image Enhancement: Improving Accuracy via Uncertainty-aware Logit Switching. CoRR abs/2403.17423 (2024) - 2023
- [i15]Masanori Yamada, Tomoya Yamashita, Shin'ya Yamaguchi, Daiki Chijiwa:
Revisiting Permutation Symmetry for Merging Models between Different Datasets. CoRR abs/2306.05641 (2023) - [i14]Shin'ya Yamaguchi, Daiki Chijiwa, Sekitoshi Kanai, Atsutoshi Kumagai, Hisashi Kashima:
Regularizing Neural Networks with Meta-Learning Generative Models. CoRR abs/2307.13899 (2023) - [i13]Satoshi Suzuki, Shin'ya Yamaguchi, Shoichiro Takeda, Sekitoshi Kanai, Naoki Makishima, Atsushi Ando, Ryo Masumura:
Adversarial Finetuning with Latent Representation Constraint to Mitigate Accuracy-Robustness Tradeoff. CoRR abs/2308.16454 (2023) - [i12]Shin'ya Yamaguchi:
Generative Semi-supervised Learning with Meta-Optimized Synthetic Samples. CoRR abs/2309.16143 (2023) - [i11]Shin'ya Yamaguchi, Takuma Fukuda:
On the Limitation of Diffusion Models for Synthesizing Training Datasets. CoRR abs/2311.13090 (2023) - 2022
- [i10]Kazuki Adachi, Shin'ya Yamaguchi:
Learning Robust Convolutional Neural Networks with Relevant Feature Focusing via Explanations. CoRR abs/2202.04237 (2022) - [i9]Shin'ya Yamaguchi, Sekitoshi Kanai, Atsutoshi Kumagai, Daiki Chijiwa, Hisashi Kashima:
Transfer Learning with Pre-trained Conditional Generative Models. CoRR abs/2204.12833 (2022) - [i8]Kazuki Adachi, Shin'ya Yamaguchi, Atsutoshi Kumagai:
Covariance-aware Feature Alignment with Pre-computed Source Statistics for Test-time Adaptation. CoRR abs/2204.13263 (2022) - [i7]Daiki Chijiwa, Shin'ya Yamaguchi, Atsutoshi Kumagai, Yasutoshi Ida:
Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks. CoRR abs/2205.15619 (2022) - [i6]Sekitoshi Kanai, Shin'ya Yamaguchi, Masanori Yamada, Hiroshi Takahashi, Yasutoshi Ida:
Switching One-Versus-the-Rest Loss to Increase the Margin of Logits for Adversarial Robustness. CoRR abs/2207.10283 (2022) - 2021
- [i5]Shin'ya Yamaguchi, Sekitoshi Kanai:
F-Drop&Match: GANs with a Dead Zone in the High-Frequency Domain. CoRR abs/2106.02343 (2021) - [i4]Daiki Chijiwa, Shin'ya Yamaguchi, Yasutoshi Ida, Kenji Umakoshi, Tomohiro Inoue:
Pruning Randomly Initialized Neural Networks with Iterative Randomization. CoRR abs/2106.09269 (2021) - 2020
- [i3]Sekitoshi Kanai, Masanori Yamada, Shin'ya Yamaguchi, Hiroshi Takahashi, Yasutoshi Ida:
Constraining Logits by Bounded Function for Adversarial Robustness. CoRR abs/2010.02558 (2020) - 2019
- [i2]Shin'ya Yamaguchi, Sekitoshi Kanai, Takeharu Eda:
Effective Data Augmentation with Multi-Domain Learning GANs. CoRR abs/1912.11597 (2019) - [i1]Shin'ya Yamaguchi, Sekitoshi Kanai, Tetsuya Shioda, Shoichiro Takeda:
Multiple Pretext-Task for Self-Supervised Learning via Mixing Multiple Image Transformations. CoRR abs/1912.11603 (2019)
Coauthor Index
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