Unsupervised learning and memory-efficient training papers accepted to NeurIPS 2019 as spotlight and poster. CfP out for ICCP 2020 / PAMI special issue on Computational Photography.
research
Generating and Exploiting Probabilistic Monocular Depth Estimates
arXiv 2019
Zhihao Xia, Patrick Sullivan, Ayan Chakrabarti
Training Image Estimators without Image Ground-Truth
NeurIPS 2019 (spotlight)
Zhihao Xia, Ayan Chakrabarti
Neural Network-Inspired Analog-to-Digital Conversion to Achieve Super-Resolution with Low-Precision RRAM Devices
ICCAD 2019
Weidong Cao, Liu Ke, Ayan Chakrabarti, Xuan Zhang
Learning to Separate Multiple Illuminants in a Single Image
CVPR 2019
Zhuo Hui, Ayan Chakrabarti, Kalyan Sunkavalli, Aswin C. Sankaranarayanan
Learning Privacy Preserving Encodings through Adversarial Training
WACV 2019
Francesco Pittaluga, Sanjeev J. Koppal, Ayan Chakrabarti
Examining the Impact of Blur on Recognition by Convolutional Networks
arXiv 2017
Igor Vasiljevic, Ayan Chakrabarti, Gregory Shakhnarovich
Learning Object Color Models from Multi-view Constraints
CVPR 2011
Trevor Owens, Kate Saenko, Ayan Chakrabarti, Ying Xiong, Todd Zickler, Trevor Darrell
Color Constancy Beyond Bags of Pixels
CVPR 2008
Ayan Chakrabarti, Keigo Hirakawa, Todd Zickler
Effective Separation of Sparse and Non-sparse Image Features for Denoising
ICASSP 2008
Ayan Chakrabarti, Keigo Hirakawa
Super-Resolution of Face Images Using Kernel PCA-Based Prior
IEEE Transactions on Multimedia 2007
Ayan Chakrabarti, A.N. Rajagopalan, Rama Chellappa
Visual Inference with Statistical Models for Color and Texture
Ph.D. Dissertation, Harvard University, 2011
Ayan Chakrabarti
