| Statistics of real-world hyperspectral images A Chakrabarti, T Zickler IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 193-200, 2011 | 482 | 2011 |
| A neural approach to blind motion deblurring A Chakrabarti European Conference on Computer Vision (ECCV), 221-235, 2016 | 398 | 2016 |
| Analyzing spatially-varying blur A Chakrabarti, T Zickler, WT Freeman IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2512-2519, 2010 | 259 | 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 | 215 | 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 | 194 | 2007 |
| Examining the impact of blur on recognition by convolutional networks I Vasiljevic, A Chakrabarti, G Shakhnarovich arXiv preprint arXiv:1611.05760, 2017 | 184 | 2017 |
| Understanding robustness of transformers for image classification S Bhojanapalli, A Chakrabarti, D Glasner, D Li, T Unterthiner, A Veit Proceedings of the IEEE/CVF international conference on computer vision …, 2021 | 170 | 2021 |
| 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 | 145 | 2016 |
| Learning sensor multiplexing design through back-propagation A Chakrabarti Advances in Neural Information Processing Systems, 3081-3089, 2016 | 115 | 2016 |
| An empirical camera model for internet color vision A Chakrabarti, D Scharstein, T Zickler British Machine Vision Conference, 2009 | 112 | 2009 |
| Depth and deblurring from a spectrally-varying depth-of-field A Chakrabarti, T Zickler European Conference on Computer Vision (ECCV), 648-661, 2012 | 91 | 2012 |
| 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 | 87 | 2015 |
| Stabilizing GAN training with multiple random projections B Neyshabur, S Bhojanapalli, A Chakrabarti arXiv preprint arXiv:1705.07831, 2018 | 85 | 2018 |
| 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 | 63 | 2019 |
| 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 | 58 | 2019 |
| Color constancy beyond bags of pixels A Chakrabarti, K Hirakawa, T Zickler IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008 | 54 | 2008 |
| 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 | 47 | 2014 |
| 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 | 46 | 2015 |
| Basis Prediction Networks for Effective Burst Denoising with Large Kernels Z Xia, F Perazzi, M Gharbi, K Sunkavalli, A Chakrabarti IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 11844 …, 2020 | 43 | 2020 |
| Color constancy by learning to predict chromaticity from luminance A Chakrabarti Advances in Neural Information Processing Systems 28, 2015 | 36 | 2015 |
Todd ZicklerProfessor of Electrical Engineering and Computer Science, Harvard UniversityVerified email at seas.harvard.edu
