| Gromov-wasserstein autoencoders N Nakagawa, R Togo, T Ogawa, M Haseyama arXiv preprint arXiv:2209.07007, 2022 | 11 | 2022 |
| Interpretable representation learning on natural image datasets via reconstruction in visual-semantic embedding space N Nakagawa, R Togo, T Ogawa, M Haseyama 2021 IEEE International Conference on Image Processing (ICIP), 2473-2477, 2021 | 2 | 2021 |
| Face Synthesis via User Manipulation of Disentangled Latent Representation N Nakagawa, R Togo, T Ogawa, M Haseyama 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE), 692-693, 2020 | 1 | 2020 |
| ConcVAE: Conceptual Representation Learning R Togo, N Nakagawa, T Ogawa, M Haseyama IEEE Transactions on Neural Networks and Learning Systems, 2024 | | 2024 |
| A Note on Disentanglement Using Deep Generative Model Based on Variational Autoencoder--Introduction of Regularization Losses Based on Metrics of Disentangled Representation N Nakagawa, R Togo, T Ogawa, M Haseyama ITE Technical Report; ITE Tech. Rep. 46 (6), 97-102, 2022 | | 2022 |
| Disentangled Representation Learning in Real-World Image Datasets via Image Segmentation Prior N Nakagawa, R Togo, T Ogawa, M Haseyama IEEE Access 9, 110880-110888, 2021 | | 2021 |
| Incorporating Domain Knowledge in VAE Learning via Exponential Dissimilarity-Dispersion Family R Togo, N Nakagawa, T Ogawa, M Haseyama | | |
Ren TogoHokkaido UniversityVerified email at lmd.ist.hokudai.ac.jp
