| Training deep networks with synthetic data: Bridging the reality gap by domain randomization J Tremblay, A Prakash, D Acuna, M Brophy, V Jampani, C Anil, T To, ... Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 1133 | 2018 |
| Gated-scnn: Gated shape cnns for semantic segmentation T Takikawa, D Acuna, V Jampani, S Fidler Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 840 | 2019 |
| Efficient interactive annotation of segmentation datasets with polygon-rnn++ D Acuna, H Ling, A Kar, S Fidler Proceedings of the IEEE conference on Computer Vision and Pattern …, 2018 | 525 | 2018 |
| Meta-sim: Learning to generate synthetic datasets A Kar, A Prakash, MY Liu, E Cameracci, J Yuan, M Rusiniak, D Acuna, ... Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 302 | 2019 |
| Structured domain randomization: Bridging the reality gap by context-aware synthetic data A Prakash, S Boochoon, M Brophy, D Acuna, E Cameracci, G State, ... 2019 International Conference on Robotics and Automation (ICRA), 7249-7255, 2019 | 222 | 2019 |
| Devil is in the edges: Learning semantic boundaries from noisy annotations D Acuna, A Kar, S Fidler Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 200 | 2019 |
| Gavriel State, Omer Shapira, and Stan Birchfield. Structured domain randomization: Bridging the reality gap by context-aware synthetic data A Prakash, S Boochoon, M Brophy, D Acuna, E Cameracci 2019 International Conference on Robotics and Automation (ICRA), 7249-7255, 2019 | 169 | 2019 |
| Neural turtle graphics for modeling city road layouts H Chu, D Li, D Acuna, A Kar, M Shugrina, X Wei, MY Liu, A Torralba, ... Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 98 | 2019 |
| Object instance annotation with deep extreme level set evolution Z Wang, D Acuna, H Ling, A Kar, S Fidler Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 92 | 2019 |
| f-domain adversarial learning: Theory and algorithms D Acuna, G Zhang, MT Law, S Fidler International Conference on Machine Learning, 66-75, 2021 | 81 | 2021 |
| Learning to generate synthetic datasets for training neural networks A Kar, A Prakash, MY Liu, DJA Marrero, AT Barriuso, S Fidler US Patent 11,610,115, 2023 | 55 | 2023 |
| Neural data server: A large-scale search engine for transfer learning data X Yan, D Acuna, S Fidler Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 52 | 2020 |
| Variational amodal object completion H Ling, D Acuna, K Kreis, SW Kim, S Fidler Advances in Neural Information Processing Systems 33, 16246-16257, 2020 | 43 | 2020 |
| Neural light field estimation for street scenes with differentiable virtual object insertion Z Wang, W Chen, D Acuna, J Kautz, S Fidler European Conference on Computer Vision, 380-397, 2022 | 37 | 2022 |
| Generation of synthetic images for training a neural network model J Tremblay, A Prakash, MA Brophy, V Jampani, C Anil, ST Birchfield, ... US Patent 10,867,214, 2020 | 29 | 2020 |
| Towards real-time detection and tracking of basketball players using deep neural networks D Acuna Proceedings of the 31st Conference on Neural Information Processing Systems …, 2017 | 29 | 2017 |
| Federated learning with heterogeneous architectures using graph hypernetworks O Litany, H Maron, D Acuna, J Kautz, G Chechik, S Fidler arXiv preprint arXiv:2201.08459, 2022 | 28 | 2022 |
| How much more data do i need? estimating requirements for downstream tasks R Mahmood, J Lucas, D Acuna, D Li, J Philion, JM Alvarez, Z Yu, S Fidler, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 28 | 2022 |
| Towards optimal strategies for training self-driving perception models in simulation D Acuna, J Philion, S Fidler Advances in Neural Information Processing Systems 34, 1686-1699, 2021 | 23 | 2021 |
| Object image completion S Fidler, DA Marrero, SW Kim, KJ Kreis, H Ling US Patent App. 17/006,702, 2022 | 20 | 2022 |
Sanja FidlerUniversity of Toronto, NVIDIAZweryfikowany adres z cs.toronto.edu