Takeru Miyato
Takeru Miyato
Preferred Networks, Inc.
Verified email at preferred.jp - Homepage
TitleCited byYear
Spectral Normalization for Generative Adversarial Networks
T Miyato, T Kataoka, M Koyama, Y Yoshida
International Conference on Learning Representations (ICLR), 2018
7922018
Virtual adversarial training: a regularization method for supervised and semi-supervised learning
T Miyato, S Maeda, S Ishii, M Koyama
IEEE transactions on pattern analysis and machine intelligence (TPAMI), 2019
3782019
Distributional Smoothing with Virtual Adversarial Training
T Miyato, S Maeda, M Koyama, K Nakae, S Ishii
International Conference on Learning Representations (ICLR), 2016
278*2016
Adversarial Training Methods for Semi-Supervised Text Classification
T Miyato, AM Dai, I Goodfellow
International Conference on Learning Representations (ICLR), 2017
2022017
cGANs with Projection Discriminator
T Miyato, M Koyama
International Conference on Learning Representations (ICLR), 2018
1042018
Learning Discrete Representations via Information Maximizing Self Augmented Training
W Hu, T Miyato, S Tokui, E Matsumoto, M Sugiyama
International Conference on Machine Learning (ICML), 2017
742017
Spectral norm regularization for improving the generalizability of deep learning
Y Yoshida, T Miyato
arXiv preprint arXiv:1705.10941, 2017
512017
Neural multi-scale image compression
KM Nakanishi, S Maeda, T Miyato, D Okanohara
Asian Conference on Computer Vision, 718-732, 2018
62018
Robustness to adversarial perturbations in learning from incomplete data
A Najafi, S Maeda, M Koyama, T Miyato
Advances in Neural Information Processing Systems, 5542-5552, 2019
52019
Spatially Controllable Image Synthesis with Internal Representation Collaging
R Suzuki, M Koyama, T Miyato, T Yonetsuji, H Zhu
arXiv preprint arXiv:1811.10153, 2018
12018
Synthetic Gradient Methods with Virtual Forward-Backward Networks
T Miyato, D Okanohara, S Maeda, K Masanori
Workshop on International Conference on Learning Representations (ICLR), 2017
12017
Unsupervised Discrete Representation Learning
W Hu, T Miyato, S Tokui, E Matsumoto, M Sugiyama
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 97-119, 2019
2019
Adaptive Sample-space & Adaptive Probability coding: a neural-network based approach for compression
K Nakanishi, S Maeda, T Miyato, M Koyama
2018
Parameter Reference Loss for Unsupervised Domain Adaptation
J Jin, RG Calland, T Miyato, BK Vogel, H Nakayama
arXiv preprint arXiv:1711.07170, 2017
2017
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Articles 1–14