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David Eigen
David Eigen
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deigen.net üzerinde doğrulanmış e-posta adresine sahip
Başlık
Alıntı yapanlar
Alıntı yapanlar
Yıl
Overfeat: Integrated recognition, localization and detection using convolutional networks
P Sermanet, D Eigen, X Zhang, M Mathieu, R Fergus, Y LeCun
arXiv preprint arXiv:1312.6229, 2013
76402013
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
42552014
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
31562015
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
5192013
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
4082019
Learning factored representations in a deep mixture of experts
D Eigen, MA Ranzato, I Sutskever
arXiv preprint arXiv:1312.4314, 2013
2772013
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
188
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
1742015
Understanding deep architectures using a recursive convolutional network
D Eigen, J Rolfe, R Fergus, Y LeCun
arXiv preprint arXiv:1312.1847, 2013
1722013
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
1082015
Nonparametric image parsing using adaptive neighbor sets
D Eigen, R Fergus
2012 IEEE Conference on Computer Vision and Pattern Recognition, 2799-2806, 2012
1062012
Unsupervised feature learning from temporal data
R Goroshin, J Bruna, J Tompson, D Eigen, Y LeCun
arXiv preprint arXiv:1504.02518, 2015
472015
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
152021
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
152016
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
142022
System and method for facilitating logo-recognition training of a recognition model
DJ Eigen, M Zeiler
US Patent 10,163,043, 2018
142018
Method and apparatus for generating dynamic microcores
DJ Eigen, DA Grunwald
US Patent 7,783,932, 2010
142010
Gradient agreement as an optimization objective for meta-learning
AE Eshratifar, D Eigen, M Pedram
arXiv preprint arXiv:1810.08178, 2018
122018
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
72019
System, method and computer-accessible medium for restoring an image taken through a window
R Fergus, D Eigen, D Krishnan
US Patent 9,672,601, 2017
62017
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