| Joint multimodal learning with deep generative models M Suzuki, K Nakayama, Y Matsuo arXiv preprint arXiv:1611.01891, 2016 | 247 | 2016 |
| Generative adversarial nets from a density ratio estimation perspective M Uehara, I Sato, M Suzuki, K Nakayama, Y Matsuo arXiv preprint arXiv:1610.02920, 2016 | 102 | 2016 |
| A survey of multimodal deep generative models M Suzuki, Y Matsuo Advanced Robotics 36 (5-6), 261-278, 2022 | 56 | 2022 |
| Neuro-serket: development of integrative cognitive system through the composition of deep probabilistic generative models T Taniguchi, T Nakamura, M Suzuki, R Kuniyasu, K Hayashi, A Taniguchi, ... New Generation Computing 38, 23-48, 2020 | 49 | 2020 |
| World models and predictive coding for cognitive and developmental robotics: frontiers and challenges T Taniguchi, S Murata, M Suzuki, D Ognibene, P Lanillos, E Ugur, ... Advanced Robotics 37 (13), 780-806, 2023 | 40 | 2023 |
| Neural machine translation with latent semantic of image and text J Toyama, M Misono, M Suzuki, K Nakayama, Y Matsuo arXiv preprint arXiv:1611.08459, 2016 | 24 | 2016 |
| A whole brain probabilistic generative model: Toward realizing cognitive architectures for developmental robots T Taniguchi, H Yamakawa, T Nagai, K Doya, M Sakagami, M Suzuki, ... Neural Networks 150, 293-312, 2022 | 23 | 2022 |
| Transfer learning based on the observation probability of each attribute M Suzuki, H Sato, S Oyama, M Kurihara 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2014 | 20 | 2014 |
| Uvton: Uv mapping to consider the 3d structure of a human in image-based virtual try-on network S Kubo, Y Iwasawa, M Suzuki, Y Matsuo Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 11 | 2019 |
| Image classification by transfer learning based on the predictive ability of each attribute M Suzuki, H Sato, S Oyama, M Kurihara Proceedings of the International MultiConference of Engineers and Computer …, 2014 | 9 | 2014 |
| Improving bi-directional generation between different modalities with variational autoencoders M Suzuki, K Nakayama, Y Matsuo arXiv preprint arXiv:1801.08702, 2018 | 7 | 2018 |
| Monophonic sound source separation by non-negative sparse autoencoders K Zen, M Suzuki, H Sato, S Oyama, M Kurihara 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2014 | 6 | 2014 |
| Pixyz: a Python library for developing deep generative models M Suzuki, T Kaneko, Y Matsuo Advanced Robotics 37 (19), 1221-1236, 2023 | 4* | 2023 |
| b-gan: Unified framework of generative adversarial networks M Uehara, I Sato, M Suzuki, K Nakayama, Y Matsuo | 4 | 2016 |
| Interaction-based disentanglement of entities for object-centric world models A Nakano, M Suzuki, Y Matsuo The Eleventh International Conference on Learning Representations, 2022 | 3 | 2022 |
| Semi-supervised multimodal learning with deep generative models M Suzuki, Y Matsuo | 3 | 2018 |
| 異なるモダリティ間の双方向生成のための深層生成モデル 鈴木雅大， 松尾豊 情報処理学会論文誌 59 (3), 859-873, 2018 | 3 | 2018 |
| 深層生成モデルを用いたマルチモーダル学習 鈴木雅大， 松尾豊 人工知能学会全国大会論文集 第 30 回 (2016), 1A3OS27a3-1A3OS27a3, 2016 | 3 | 2016 |
| 深層ニューラルネットワークの中間層出力を利用した半教師あり分布外検知 岡本弘野， 鈴木雅大， 松尾豊 情報処理学会論文誌 62 (4), 1142-1151, 2021 | 2 | 2021 |
| Iterative image inpainting with structural similarity mask for anomaly detection H Nakanishi, M Suzuki, Y Matsuo | 2 | 2020 |
Satoshi OyamaNagoya City UniversityVerified email at ds.nagoya-cu.ac.jp
