| beta-vae: Learning basic visual concepts with a constrained variational framework I Higgins, L Matthey, A Pal, C Burgess, X Glorot, M Botvinick, S Mohamed, ... International conference on learning representations, 2017 | 5938 | 2017 |
| Understanding disentangling in -VAE CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ... arXiv preprint arXiv:1804.03599, 2018 | 1313 | 2018 |
| Towards a definition of disentangled representations I Higgins, D Amos, D Pfau, S Racaniere, L Matthey, D Rezende, ... arXiv preprint arXiv:1812.02230, 2018 | 611 | 2018 |
| Monet: Unsupervised scene decomposition and representation CP Burgess, L Matthey, N Watters, R Kabra, I Higgins, M Botvinick, ... arXiv preprint arXiv:1901.11390, 2019 | 574 | 2019 |
| Multi-object representation learning with iterative variational inference K Greff, RL Kaufman, R Kabra, N Watters, C Burgess, D Zoran, L Matthey, ... International conference on machine learning, 2424-2433, 2019 | 553 | 2019 |
| Darla: Improving zero-shot transfer in reinforcement learning I Higgins, A Pal, A Rusu, L Matthey, C Burgess, A Pritzel, M Botvinick, ... International conference on machine learning, 1480-1490, 2017 | 549 | 2017 |
| dsprites: Disentanglement testing sprites dataset L Matthey, I Higgins, D Hassabis, A Lerchner | 470 | 2017 |
| Early visual concept learning with unsupervised deep learning I Higgins, L Matthey, X Glorot, A Pal, B Uria, C Blundell, S Mohamed, ... arXiv preprint arXiv:1606.05579, 2016 | 209 | 2016 |
| Spatial broadcast decoder: A simple architecture for learning disentangled representations in vaes N Watters, L Matthey, CP Burgess, A Lerchner arXiv preprint arXiv:1901.07017, 2019 | 173 | 2019 |
| Scan: Learning hierarchical compositional visual concepts I Higgins, N Sonnerat, L Matthey, A Pal, CP Burgess, M Bosnjak, ... arXiv preprint arXiv:1707.03389, 2017 | 148 | 2017 |
| Life-long disentangled representation learning with cross-domain latent homologies A Achille, T Eccles, L Matthey, C Burgess, N Watters, A Lerchner, ... Advances in Neural Information Processing Systems 31, 2018 | 142 | 2018 |
| Cobra: Data-efficient model-based rl through unsupervised object discovery and curiosity-driven exploration N Watters, L Matthey, M Bosnjak, CP Burgess, A Lerchner arXiv preprint arXiv:1905.09275, 2019 | 132 | 2019 |
| Unsupervised Model Selection for Variational Disentangled Representation Learning S Duan, L Matthey, A Saraiva, N Watters, CP Burgess, A Lerchner, ... arXiv preprint arXiv:1905.12614, 2019 | 97 | 2019 |
| Simone: View-invariant, temporally-abstracted object representations via unsupervised video decomposition R Kabra, D Zoran, G Erdogan, L Matthey, A Creswell, M Botvinick, ... Advances in Neural Information Processing Systems 34, 20146-20159, 2021 | 82 | 2021 |
| Understanding disentangling in β-VAE. arXiv 2018 CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ... arXiv preprint arXiv:1804.03599, 1804 | 81 | 1804 |
| Multi-object datasets R Kabra, C Burgess, L Matthey, RL Kaufman, K Greff, M Reynolds, ... DeepMind 5 (6), 7, 2019 | 80 | 2019 |
| Response variability in balanced cortical networks A Lerchner, C Ursta, J Hertz, M Ahmadi, P Ruffiot, S Enemark Neural computation 18 (3), 634-659, 2006 | 68 | 2006 |
| dSprites: disentanglement testing sprites dataset (2017) L Matthey, I Higgins, D Hassabis, A Lerchner URL https://github. com/deepmind/dsprites-dataset, 27, 2020 | 65 | 2020 |
| Parts: Unsupervised segmentation with slots, attention and independence maximization D Zoran, R Kabra, A Lerchner, DJ Rezende Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 52 | 2021 |
| Soda: Bottleneck diffusion models for representation learning DA Hudson, D Zoran, M Malinowski, AK Lampinen, A Jaegle, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 37 | 2024 |
Loic MattheyDeepMind在 google.com 的電子郵件地址已通過驗證
