| Supervised contrastive learning P Khosla, P Teterwak, C Wang, A Sarna, Y Tian, P Isola, A Maschinot, ... Advances in neural information processing systems 33, 18661-18673, 2020 | 4272 | 2020 |
| Contrastive multiview coding Y Tian, D Krishnan, P Isola Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 2469 | 2020 |
| Deconvolutional networks MD Zeiler, D Krishnan, GW Taylor, R Fergus 2010 IEEE Computer Society Conference on computer vision and pattern …, 2010 | 2262 | 2010 |
| Unsupervised pixel-level domain adaptation with generative adversarial networks K Bousmalis, N Silberman, D Dohan, D Erhan, D Krishnan Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 1865 | 2017 |
| Fast image deconvolution using hyper-Laplacian priors D Krishnan, R Fergus Advances in neural information processing systems 22, 2009 | 1682 | 2009 |
| Domain separation networks K Bousmalis, G Trigeorgis, N Silberman, D Krishnan, D Erhan Advances in neural information processing systems 29, 2016 | 1663 | 2016 |
| Blind deconvolution using a normalized sparsity measure D Krishnan, T Tay, R Fergus CVPR 2011, 233-240, 2011 | 1346 | 2011 |
| What makes for good views for contrastive learning? Y Tian, C Sun, B Poole, D Krishnan, C Schmid, P Isola Advances in neural information processing systems 33, 6827-6839, 2020 | 1254 | 2020 |
| Contrastive representation distillation Y Tian, D Krishnan, P Isola arXiv preprint arXiv:1910.10699, 2019 | 1121 | 2019 |
| Rethinking few-shot image classification: a good embedding is all you need? Y Tian, Y Wang, D Krishnan, JB Tenenbaum, P Isola Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 952 | 2020 |
| Fantastic generalization measures and where to find them Y Jiang, B Neyshabur, H Mobahi, D Krishnan, S Bengio arXiv preprint arXiv:1912.02178, 2019 | 586 | 2019 |
| 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 | 543 | 2013 |
| Visualizing dataflow graphs of deep learning models in tensorflow K Wongsuphasawat, D Smilkov, J Wexler, J Wilson, D Mane, D Fritz, ... IEEE transactions on visualization and computer graphics 24 (1), 1-12, 2017 | 395 | 2017 |
| Muse: Text-to-image generation via masked generative transformers H Chang, H Zhang, J Barber, AJ Maschinot, J Lezama, L Jiang, MH Yang, ... arXiv preprint arXiv:2301.00704, 2023 | 322 | 2023 |
| Adversarial robustness through local linearization C Qin, J Martens, S Gowal, D Krishnan, K Dvijotham, A Fawzi, S De, ... Advances in neural information processing systems 32, 2019 | 318 | 2019 |
| Large margin deep networks for classification G Elsayed, D Krishnan, H Mobahi, K Regan, S Bengio Advances in neural information processing systems 31, 2018 | 311 | 2018 |
| Reflection removal using ghosting cues YC Shih, D Krishnan, F Durand, WT Freeman Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 270 | 2015 |
| Crisp boundary detection using pointwise mutual information P Isola, D Zoran, D Krishnan, EH Adelson Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland …, 2014 | 237 | 2014 |
| A margin-based measure of generalization for deep networks Y Jiang, D Krishnan, H Mobahi, S Bengio ICLR, 2019 | 215* | 2019 |
| Learning ordinal relationships for mid-level vision D Zoran, P Isola, D Krishnan, WT Freeman Proceedings of the IEEE international conference on computer vision, 388-396, 2015 | 206 | 2015 |
