Spectral Normalization for Generative Adversarial Networks T Miyato, T Kataoka, M Koyama, Y Yoshida International Conference on Learning Representations (ICLR), 2018 | 5207 | 2018 |
Virtual adversarial training: a regularization method for supervised and semi-supervised learning T Miyato, S Maeda, K Masanori, S Ishii IEEE transactions on pattern analysis and machine intelligence 41 (8), 1979-1993, 2018 | 3023 | 2018 |
Adversarial Training Methods for Semi-Supervised Text Classification T Miyato, AM Dai, I Goodfellow International Conference on Learning Representations (ICLR), 2017 | 1295 | 2017 |
cGANs with Projection Discriminator T Miyato, M Koyama International Conference on Learning Representations (ICLR), 2018 | 624 | 2018 |
Distributional smoothing with virtual adversarial training T Miyato, S Maeda, M Koyama, K Nakae, S Ishii arXiv preprint arXiv:1507.00677, 2015 | 565 | 2015 |
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 | 532 | 2017 |
Spectral norm regularization for improving the generalizability of deep learning Y Yoshida, T Miyato arXiv preprint arXiv:1705.10941, 2017 | 365 | 2017 |
Robustness to adversarial perturbations in learning from incomplete data A Najafi, S Maeda, M Koyama, T Miyato Advances in Neural Information Processing Systems 32, 2019 | 124 | 2019 |
Spatially controllable image synthesis with internal representation collaging R Suzuki, M Koyama, T Miyato, T Yonetsuji, H Zhu arXiv preprint arXiv:1811.10153, 2018 | 44 | 2018 |
Neural multi-scale image compression KM Nakanishi, S Maeda, T Miyato, D Okanohara Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth …, 2019 | 39 | 2019 |
Unsupervised learning of equivariant structure from sequences T Miyato, M Koyama, K Fukumizu Advances in Neural Information Processing Systems 35, 768-781, 2022 | 12 | 2022 |
Image generation method, image generation apparatus, and image generation program T Miyato US Patent 11,048,999, 2021 | 8 | 2021 |
Data discriminator training method, data discriminator training apparatus, non-transitory computer readable medium, and training method T Miyato US Patent 11,593,663, 2023 | 7 | 2023 |
Gta: A geometry-aware attention mechanism for multi-view transformers T Miyato, B Jaeger, M Welling, A Geiger arXiv preprint arXiv:2310.10375, 2023 | 4 | 2023 |
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 | 4 | 2017 |
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 | 3 | 2019 |
Neural fourier transform: A general approach to equivariant representation learning M Koyama, K Fukumizu, K Hayashi, T Miyato arXiv preprint arXiv:2305.18484, 2023 | 2 | 2023 |
Invariance-adapted decomposition and lasso-type contrastive learning M Koyama, T Miyato, K Fukumizu arXiv preprint arXiv:2210.07413, 2022 | 1 | 2022 |
Apparatus and method for editing data and program R Suzuki, T Miyato, T Yonetsuji US Patent 11,373,350, 2022 | 1 | 2022 |
Contrastive representation learning with trainable augmentation channel M Koyama, K Minami, T Miyato, Y Gal arXiv preprint arXiv:2111.07679, 2021 | 1 | 2021 |