Ayan Chakrabarti

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Todd ZicklerProfessor of Electrical Engineering and Computer Science, Harvard UniversityVerified email at seas.harvard.edu
Keigo HirakawaUniversity of DaytonVerified email at udayton.edu
Ying XiongPhD, Harvard UniversityVerified email at seas.harvard.edu
William T. FreemanProfessor of Computer Science, MITVerified email at mit.edu
Greg ShakhnarovichProfessor, TTI-ChicagoVerified email at ttic.edu
Daniel ScharsteinProfessor of Computer Science, Middlebury CollegeVerified email at middlebury.edu
Kalyan SunkavalliAdobe ResearchVerified email at adobe.com
Zhihao XiaPhD student, Washington University in St. LouisVerified email at wustl.edu
Steven J. GortlerHarvard UniversityVerified email at cs.harvard.edu
A N RajagopalanIndian Institute of Technology MadrasVerified email at ee.iitm.ac.in
Rama ChellappaBloomberg Distinguished Professor, Johns Hopkins UniversityVerified email at jhu.edu
Trevor DarrellProfessor of Computer Science, U.C. BerkeleyVerified email at eecs.berkeley.edu
Kate SaenkoBoston University & MIT-IBM Watson AI LabVerified email at bu.edu
Sanjeev KoppalAssistant Professor, University of FloridaVerified email at ece.ufl.edu
Francesco PittalugaNEC Labs AmericaVerified email at nec-labs.com
Xuan ZhangAssistant Professor, Washington UniversityVerified email at wustl.edu
Srinadh BhojanapalliResearch Scientist, Google ResearchVerified email at google.com
Igor VasiljevicToyota Technological Institute at ChicagoVerified email at ttic.edu
Weidong CaoPh.D student of Electrical and System Engineering, Washington University in Saint.LouisVerified email at wustl.edu
Matthew R. WalterToyota Technological Institute at ChicagoVerified email at ttic.edu

Ayan Chakrabarti

Google Research; Washington University in St. Louis

Verified email at google.com - Homepage

Computer Vision Machine Learning Computational Photography


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Statistics of real-world hyperspectral images A Chakrabarti, T Zickler IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 193-200, 2011	329	2011
A neural approach to blind motion deblurring A Chakrabarti European Conference on Computer Vision (ECCV), 221-235, 2016	238	2016
Analyzing spatially-varying blur A Chakrabarti, T Zickler, WT Freeman IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2512-2519, 2010	227	2010
Color constancy with spatio-spectral statistics A Chakrabarti, K Hirakawa, T Zickler IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (8), 1509-1519, 2012	199	2012
Super-resolution of face images using kernel PCA-based prior A Chakrabarti, AN Rajagopalan, R Chellappa IEEE Transactions on Multimedia 9 (4), 888-892, 2007	173	2007
Examining the impact of blur on recognition by convolutional networks I Vasiljevic, A Chakrabarti, G Shakhnarovich arXiv preprint arXiv:1611.05760, 2017	99	2017
Depth from a single image by harmonizing overcomplete local network predictions A Chakrabarti, J Shao, G Shakhnarovich Advances in Neural Information Processing Systems, 2658-2666, 2016	94	2016
An empirical camera model for internet color vision A Chakrabarti, D Scharstein, T Zickler British Machine Vision Conference, 2009	94	2009
Depth and deblurring from a spectrally-varying depth-of-field A Chakrabarti, T Zickler European Conference on Computer Vision (ECCV), 648-661, 2012	74	2012
Learning sensor multiplexing design through back-propagation A Chakrabarti Advances in Neural Information Processing Systems, 3081-3089, 2016	66	2016
From shading to local shape Y Xiong, A Chakrabarti, R Basri, SJ Gortler, DW Jacobs, T Zickler IEEE Transactions on Pattern Analysis and Machine Intelligence 37 (1), 67-79, 2015	64	2015
Color constancy beyond bags of pixels A Chakrabarti, K Hirakawa, T Zickler IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008	52	2008
Stabilizing GAN training with multiple random projections B Neyshabur, S Bhojanapalli, A Chakrabarti arXiv preprint arXiv:1705.07831, 2018	44	2018
Low-level vision by consensus in a spatial hierarchy of regions A Chakrabarti, Y Xiong, SJ Gortler, T Zickler IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015	43	2015
Modeling radiometric uncertainty for vision with tone-mapped color images A Chakrabarti, Y Xiong, B Sun, T Darrell, D Scharstein, T Zickler, ... IEEE Transactions on Pattern Analysis and Machine Intelligence 36 (11), 2185 …, 2014	40	2014
Color constancy by learning to predict chromaticity from luminance A Chakrabarti arXiv preprint arXiv:1506.02167, 2015	28	2015
Learning privacy preserving encodings through adversarial training F Pittaluga, S Koppal, A Chakrabarti IEEE Winter Conference on Applications of Computer Vision (WACV), 791-799, 2019	27	2019
Learning object color models from multi-view constraints T Owens, K Saenko, A Chakrabarti, Y Xiong, T Zickler, T Darrell IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 169-176, 2011	25	2011
Jointly learning to construct and control agents using deep reinforcement learning C Schaff, D Yunis, A Chakrabarti, MR Walter 2019 International Conference on Robotics and Automation (ICRA), 9798-9805, 2019	24	2019
Rethinking color cameras A Chakrabarti, WT Freeman, T Zickler IEEE International Conference on Computational Photography (ICCP), 1-8, 2014	24	2014

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