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ORCIDNathan Silberman
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2020 – today
- 2020
[c13]Igor Lovchinsky, Alon Daks, Israel Malkin, Pouya Samangouei, Ardavan Saeedi, Yang Liu, Swami Sankaranarayanan, Tomer Gafner, Ben Sternlieb, Patrick Maher, Nathan Silberman:
Discrepancy Ratio: Evaluating Model Performance When Even Experts Disagree on the Truth. ICLR 2020
2010 – 2019
- 2019
- [j1]Zbigniew Wojna
, Vittorio Ferrari, Sergio Guadarrama, Nathan Silberman, Liang-Chieh Chen, Alireza Fathi, Jasper R. R. Uijlings:
The Devil is in the Decoder: Classification, Regression and GANs. Int. J. Comput. Vis. 127(11-12): 1694-1706 (2019) - [c12]Ryutaro Tanno, Ardavan Saeedi, Swami Sankaranarayanan, Daniel C. Alexander, Nathan Silberman:
Learning From Noisy Labels by Regularized Estimation of Annotator Confusion. CVPR 2019: 11244-11253 - [i4]Ryutaro Tanno, Ardavan Saeedi, Swami Sankaranarayanan, Daniel C. Alexander, Nathan Silberman:
Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion. CoRR abs/1902.03680 (2019) - 2018
- [c11]Pouya Samangouei, Ardavan Saeedi, Liam Nakagawa, Nathan Silberman:
ExplainGAN: Model Explanation via Decision Boundary Crossing Transformations. ECCV (10) 2018: 681-696 - 2017
- [c10]Zbigniew Wojna, Jasper R. R. Uijlings, Sergio Guadarrama, Nathan Silberman, Liang-Chieh Chen, Alireza Fathi, Vittorio Ferrari:
The Devil is in the Decoder. BMVC 2017 - [c9]Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, Dilip Krishnan:
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks. CVPR 2017: 95-104 - [i3]Zbigniew Wojna, Vittorio Ferrari, Sergio Guadarrama, Nathan Silberman, Liang-Chieh Chen, Alireza Fathi, Jasper R. R. Uijlings:
The Devil is in the Decoder. CoRR abs/1707.05847 (2017) - 2016
- [c8]Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, Dumitru Erhan:
Domain Separation Networks. NIPS 2016: 343-351 - [i2]Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, Dumitru Erhan:
Domain Separation Networks. CoRR abs/1608.06019 (2016) - [i1]Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, Dilip Krishnan:
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks. CoRR abs/1612.05424 (2016) - 2015
- [b1]Nathan Silberman:
Instance Segmentation of RGBD Scenes. New York University, USA, 2015 - [c7]Austin Myers, Nick Johnston, Vivek Rathod, Anoop Korattikara, Alexander N. Gorban, Nathan Silberman, Sergio Guadarrama, George Papandreou, Jonathan Huang, Kevin Murphy:
Im2Calories: Towards an Automated Mobile Vision Food Diary. ICCV 2015: 1233-1241 - 2014
- [c6]Nathan Silberman, Lior Shapira, Ran Gal, Pushmeet Kohli:
A Contour Completion Model for Augmenting Surface Reconstructions. ECCV (3) 2014: 488-503 - [c5]Nathan Silberman, David A. Sontag, Rob Fergus:
Instance Segmentation of Indoor Scenes Using a Coverage Loss. ECCV (1) 2014: 616-631 - 2012
- [c4]Nathan Silberman, Derek Hoiem, Pushmeet Kohli, Rob Fergus:
Indoor Segmentation and Support Inference from RGBD Images. ECCV (5) 2012: 746-760 - 2011
- [c3]Nathan Silberman, Rob Fergus:
Indoor scene segmentation using a structured light sensor. ICCV Workshops 2011: 601-608 - 2010
- [c2]Nathan Silberman, Kristy Ahrlich, Rob Fergus, Lakshminarayanan Subramanian:
Case for Automated Detection of Diabetic Retinopathy. AAAI Spring Symposium: Artificial Intelligence for Development 2010
2000 – 2009
- 2009
- [c1]Gideon Mann, Ryan T. McDonald, Mehryar Mohri, Nathan Silberman, Dan Walker:
Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models. NIPS 2009: 1231-1239
Coauthor Index
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