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ORCIDTakeru Miyato
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Journal Articles
- 2019
[j1]Takeru Miyato
, Shin-ichi Maeda, Masanori Koyama, Shin Ishii:
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning. IEEE Trans. Pattern Anal. Mach. Intell. 41(8): 1979-1993 (2019)
Conference and Workshop Papers
- 2024
- [c11]Masanori Koyama, Kenji Fukumizu, Kohei Hayashi, Takeru Miyato:
Neural Fourier Transform: A General Approach to Equivariant Representation Learning. ICLR 2024 - [c10]Takeru Miyato, Bernhard Jaeger, Max Welling, Andreas Geiger:
GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers. ICLR 2024 - 2022
- [c9]Takeru Miyato, Masanori Koyama, Kenji Fukumizu:
Unsupervised Learning of Equivariant Structure from Sequences. NeurIPS 2022 - 2019
- [c8]Amir Najafi, Shin-ichi Maeda, Masanori Koyama, Takeru Miyato:
Robustness to Adversarial Perturbations in Learning from Incomplete Data. NeurIPS 2019: 5542-5552 - 2018
- [c7]Ken M. Nakanishi, Shin-ichi Maeda, Takeru Miyato, Daisuke Okanohara:
Neural Multi-scale Image Compression. ACCV (6) 2018: 718-732 - [c6]Takeru Miyato, Masanori Koyama:
cGANs with Projection Discriminator. ICLR (Poster) 2018 - [c5]Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida:
Spectral Normalization for Generative Adversarial Networks. ICLR 2018 - 2017
- [c4]Takeru Miyato, Andrew M. Dai, Ian J. Goodfellow:
Adversarial Training Methods for Semi-Supervised Text Classification. ICLR (Poster) 2017 - [c3]Takeru Miyato, Daisuke Okanohara, Shin-ichi Maeda, Masanori Koyama:
Synthetic Gradient Methods with Virtual Forward-Backward Networks. ICLR (Workshop) 2017 - [c2]Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, Masashi Sugiyama:
Learning Discrete Representations via Information Maximizing Self-Augmented Training. ICML 2017: 1558-1567 - 2016
- [c1]Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, Shin Ishii:
Distributional Smoothing by Virtual Adversarial Examples. ICLR (Poster) 2016
Parts in Books or Collections
- 2019
- [p1]Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, Masashi Sugiyama:
Unsupervised Discrete Representation Learning. Explainable AI 2019: 97-119
Informal and Other Publications
- 2023
- [i15]Masanori Koyama, Kenji Fukumizu, Kohei Hayashi, Takeru Miyato:
Neural Fourier Transform: A General Approach to Equivariant Representation Learning. CoRR abs/2305.18484 (2023) - [i14]Takeru Miyato, Bernhard Jaeger, Max Welling, Andreas Geiger:
GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers. CoRR abs/2310.10375 (2023) - 2022
- [i13]Takeru Miyato, Masanori Koyama, Kenji Fukumizu:
Unsupervised Learning of Equivariant Structure from Sequences. CoRR abs/2210.05972 (2022) - [i12]Masanori Koyama, Takeru Miyato, Kenji Fukumizu:
Invariance-adapted decomposition and Lasso-type contrastive learning. CoRR abs/2210.07413 (2022) - 2021
- [i11]Masanori Koyama, Kentaro Minami, Takeru Miyato, Yarin Gal:
Contrastive Representation Learning with Trainable Augmentation Channel. CoRR abs/2111.07679 (2021) - 2019
- [i10]Amir Najafi, Shin-ichi Maeda, Masanori Koyama, Takeru Miyato:
Robustness to Adversarial Perturbations in Learning from Incomplete Data. CoRR abs/1905.13021 (2019) - 2018
- [i9]Takeru Miyato, Masanori Koyama:
cGANs with Projection Discriminator. CoRR abs/1802.05637 (2018) - [i8]Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida:
Spectral Normalization for Generative Adversarial Networks. CoRR abs/1802.05957 (2018) - [i7]Ken Nakanishi, Shin-ichi Maeda, Takeru Miyato, Daisuke Okanohara:
Neural Multi-scale Image Compression. CoRR abs/1805.06386 (2018) - [i6]Ryohei Suzuki, Masanori Koyama, Takeru Miyato, Taizan Yonetsuji:
Collaging on Internal Representations: An Intuitive Approach for Semantic Transfiguration. CoRR abs/1811.10153 (2018) - 2017
- [i5]Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, Masashi Sugiyama:
Learning Discrete Representations via Information Maximizing Self Augmented Training. CoRR abs/1702.08720 (2017) - [i4]Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Shin Ishii:
Virtual Adversarial Training: a Regularization Method for Supervised and Semi-supervised Learning. CoRR abs/1704.03976 (2017) - [i3]Yuichi Yoshida, Takeru Miyato:
Spectral Norm Regularization for Improving the Generalizability of Deep Learning. CoRR abs/1705.10941 (2017) - [i2]Jiren Jin, Richard G. Calland, Takeru Miyato, Brian K. Vogel, Hideki Nakayama:
Parameter Reference Loss for Unsupervised Domain Adaptation. CoRR abs/1711.07170 (2017) - 2016
- [i1]Takeru Miyato, Andrew M. Dai, Ian J. Goodfellow:
Virtual Adversarial Training for Semi-Supervised Text Classification. CoRR abs/1605.07725 (2016)
Coauthor Index
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