| Overfeat: Integrated Recognition, Localization and Detection Using Convolutional networks P Sermanet arXiv preprint arXiv:1312.6229, 2013 | 7888 | 2013 |
| Depth map prediction from a single image using a multi-scale deep network D Eigen, C Puhrsch, R Fergus Advances in neural information processing systems 27, 2014 | 4817 | 2014 |
| Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture D Eigen, R Fergus Proceedings of the IEEE international conference on computer vision, 2650-2658, 2015 | 3400 | 2015 |
| Restoring an image taken through a window covered with dirt or rain D Eigen, D Krishnan, R Fergus Proceedings of the IEEE international conference on computer vision, 633-640, 2013 | 548 | 2013 |
| Finding task-relevant features for few-shot learning by category traversal H Li, D Eigen, S Dodge, M Zeiler, X Wang Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 447 | 2019 |
| Learning factored representations in a deep mixture of experts D Eigen, MA Ranzato, I Sutskever arXiv preprint arXiv:1312.4314, 2013 | 363 | 2013 |
| Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv 2013 P Sermanet, D Eigen, X Zhang, M Mathieu, R Fergus, Y LeCun arXiv preprint arXiv:1312.6229, 0 | 203 | |
| Unsupervised learning of spatiotemporally coherent metrics R Goroshin, J Bruna, J Tompson, D Eigen, Y LeCun Proceedings of the IEEE international conference on computer vision, 4086-4093, 2015 | 188 | 2015 |
| Understanding deep architectures using a recursive convolutional network D Eigen, J Rolfe, R Fergus, Y LeCun arXiv preprint arXiv:1312.1847, 2013 | 180 | 2013 |
| End-to-end integration of a convolution network, deformable parts model and non-maximum suppression L Wan, D Eigen, R Fergus Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015 | 114 | 2015 |
| Nonparametric image parsing using adaptive neighbor sets D Eigen, R Fergus 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2799-2806, 2012 | 105 | 2012 |
| Unsupervised feature learning from temporal data R Goroshin, J Bruna, J Tompson, D Eigen, Y LeCun arXiv preprint arXiv:1504.02518, 2015 | 47 | 2015 |
| Prediction-model-based mapping and/or search using a multi-data-type vector space M Zeiler, D Eigen, R Compton, C Fox US Patent 11,281,962, 2022 | 17 | 2022 |
| Coarse2Fine: a two-stage training method for fine-grained visual classification AE Eshratifar, D Eigen, M Gormish, M Pedram Machine Vision and Applications 32 (2), 49, 2021 | 16 | 2021 |
| Gradient agreement as an optimization objective for meta-learning AE Eshratifar, D Eigen, M Pedram arXiv preprint arXiv:1810.08178, 2018 | 15 | 2018 |
| System, method and computer-accessible medium for restoring an image taken through a window R Fergus, D Eigen, D Krishnan US Patent 9,373,160, 2016 | 15 | 2016 |
| System and method for facilitating logo-recognition training of a recognition model DJ Eigen, M Zeiler US Patent 10,163,043, 2018 | 14 | 2018 |
| Method and apparatus for generating dynamic microcores DJ Eigen, DA Grunwald US Patent 7,783,932, 2010 | 14 | 2010 |
| A meta-learning approach for custom model training AE Eshratifar, MS Abrishami, D Eigen, M Pedram Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 9937-9938, 2019 | 8 | 2019 |
| Efficient training of deep convolutional neural networks by augmentation in embedding space MS Abrishami, AE Eshratifar, D Eigen, Y Wang, S Nazarian, M Pedram 2020 21st International Symposium on Quality Electronic Design (ISQED), 347-351, 2020 | 6 | 2020 |
