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Dilip Krishnan

Active 1996–2025

61
Papers
25,309
Citations
34
h-index
48
i10-index

Citations

Citations per year for Dilip Krishnan1960: 1 citations1971: 1 citations1985: 1 citations1994: 1 citations1997: 2 citations1998: 3 citations1999: 9 citations2000: 2 citations2001: 6 citations2002: 3 citations2004: 5 citations2005: 1 citations2006: 4 citations2007: 6 citations2008: 6 citations2009: 6 citations2010: 25 citations2011: 49 citations2012: 75 citations2013: 117 citations2014: 196 citations2015: 262 citations2016: 304 citations2017: 472 citations2018: 681 citations2019: 941 citations2020: 1,255 citations2021: 1,547 citations2022: 1,061 citations2023: 1,001 citations2024: 1,027 citations2025: 767 citations2026: 170 citations1961–1970: no citations, so these years are not shown1972–1984: no citations, so these years are not shown1986–1993: no citations, so these years are not shown1995–1996: no citations, so these years are not shown2003: no citations, so this year is not shown

Citation sources

Countries

World map of the countries and regions citing this authorChina: 3,292 citing papers, 31.4% of this breakdownUnited States: 2,216 citing papers, 21.2% of this breakdownUnited Kingdom: 568 citing papers, 5.4% of this breakdownSouth Korea: 383 citing papers, 3.7% of this breakdownHong Kong: 348 citing papers, 3.3% of this breakdownGermany: 343 citing papers, 3.3% of this breakdownAustralia: 320 citing papers, 3.1% of this breakdownCanada: 317 citing papers, 3% of this breakdownSingapore: 292 citing papers, 2.8% of this breakdownFrance: 247 citing papers, 2.4% of this breakdownIndia: 215 citing papers, 2% of this breakdownJapan: 206 citing papers, 2% of this breakdown
0%31.4%Other 16.4%

Institutions

Fields

  • Computer Science79.1%
  • Engineering11.3%
  • Medicine2.8%
  • Biochemistry, Genetics and Molecular Biology1.3%
  • Neuroscience0.9%
  • Physics and Astronomy0.9%
  • Other3.7%

Topics

  • Domain Adaptation and Few-Shot Learning8%
  • Advanced Image Processing Techniques6.7%
  • Multimodal Machine Learning Applications4.7%
  • Image and Signal Denoising Methods4.4%
  • Advanced Neural Network Applications4.3%
  • Advanced Vision and Imaging4%
  • Other67.9%

Coauthors

All papers

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  1. Muse: Text-To-Image Generation via Masked Generative Transformers

    Authors: Huiwen Chang, Han Zhang, Jarred Barber, Aaron Maschinot, José Lezama, Lu Jiang, Ming-Hsuan Yang, Kevin Patrick Murphy, William T. Freeman, Michael Rubinstein, Yuanzhen Li, Dilip Krishnan - ICML 2023 cited by 785

  2. Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks

    Authors: Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, Dilip Krishnan - IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 cited by 1,633

  3. StyleDrop: Text-to-Image Generation in Any Style

    Authors: Kihyuk Sohn, Nataniel Ruiz, Kimin Lee, Daniel Castro Chin, Irina Blok, Huiwen Chang, Jarred Barber, Lu Jiang, Glenn Entis, Yuanzhen Li, Yuan Hao, Irfan Essa, Michael Rubinstein, Dilip Krishnan - arXiv (Cornell University), CoRR 2023 cited by 160

  4. Contrastive Representation Distillation

    Authors: Yonglong Tian, Dilip Krishnan, Phillip Isola - ICLR 2020 cited by 1,374

  5. Deconvolutional networks

    Authors: Matthew D. Zeiler, Dilip Krishnan, Graham W. Taylor, Robert Fergus - IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010 cited by 1,714

  6. What Makes for Good Views for Contrastive Learning?

    Authors: Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, Phillip Isola - NeurIPS 2020 cited by 1,584

  7. Supervised Contrastive Learning

    Authors: Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, Dilip Krishnan - NeurIPS 2020 cited by 6,563

  8. Fantastic Generalization Measures and Where to Find Them

    Authors: Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, Samy Bengio - International Conference on Learning Representations, ICLR 2020 cited by 743

  9. Blind deconvolution using a normalized sparsity measure

    Authors: Dilip Krishnan, Terence Tay, Rob Fergus - CVPR 2011 cited by 1,100

  10. Improving CLIP Training with Language Rewrites

    Authors: Lijie Fan, Dilip Krishnan, Phillip Isola, Dina Katabi, Yonglong Tian - Advances in Neural Information Processing Systems 36, NeurIPS 2023 cited by 296

  11. Contrastive Multiview Coding

    Authors: Yonglong Tian, Dilip Krishnan, Phillip Isola - Lecture notes in computer science, ECCV (11) 2020 cited by 1,775

  12. StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners

    Authors: Yonglong Tian, Lijie Fan, Phillip Isola, Huiwen Chang, Dilip Krishnan - NeurIPS 2023 cited by 238

  13. Simplified Transfer Learning for Chest Radiography Models Using Less Data

    Authors: Andrew Sellergren, Christina Chen, Zaid Nabulsi, Yuanzhen Li, Aaron Maschinot, Aaron Sarna, Jenny Huang, Charles T. Lau, Sreenivasa Raju Kalidindi, Mozziyar Etemadi, Florencia Garcia-Vicente, David Melnick, Yun Liu, Krishnan Eswaran, Daniel Tse, Neeral Beladia, Dilip Krishnan, Shravya Shetty - Radiology 2022 cited by 56

  14. Restoring an Image Taken through a Window Covered with Dirt or Rain

    Authors: David Eigen, Dilip Krishnan, Rob Fergus - IEEE International Conference on Computer Vision, ICCV 2013 cited by 475

  15. Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow

    Authors: Kanit Wongsuphasawat, Daniel Smilkov, James Wexler, Jimbo Wilson, Dandelion Mané, Doug Fritz, Dilip Krishnan, Fernanda B. Viégas, Martin Wattenberg - IEEE Transactions on Visualization and Computer Graphics, IEEE Trans. Vis. Comput. Graph. 2017 cited by 331

  16. Reflection removal using ghosting cues

    Authors: Yichang Shih, Dilip Krishnan, Frédo Durand, William T. Freeman - IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015 cited by 218

  17. Fast Image Deconvolution using Hyper-Laplacian Priors

    Authors: Dilip Krishnan, Rob Fergus - http://cs.nyu.edu/%7Edilip/research/papers/fid_nips09_technote.pdf 2009 cited by 1,474

  18. Scaling Laws of Synthetic Images for Model Training ... for Now

    Authors: Lijie Fan, Kaifeng Chen, Dilip Krishnan, Dina Katabi, Phillip Isola, Yonglong Tian - IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024 cited by 32

  19. Learning Vision from Models Rivals Learning Vision from Data

    Authors: Yonglong Tian, Lijie Fan, Kaifeng Chen, Dina Katabi, Dilip Krishnan, Phillip Isola - IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024 cited by 24

  20. Adversarial Robustness through Local Linearization

    Authors: Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy Dvijotham, Alhussein Fawzi, Soham De, Robert Stanforth, Pushmeet Kohli - Neural Information Processing Systems, NeurIPS 2019 cited by 333

  21. Predicting the Generalization Gap in Deep Networks with Margin Distributions

    Authors: Yiding Jiang, Dilip Krishnan, Hossein Mobahi, Samy Bengio - International Conference on Learning Representations, ICLR (Poster) 2018 cited by 224

  22. Rethinking Few-Shot Image Classification: A Good Embedding is All You Need?

    Authors: Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B. Tenenbaum, Phillip Isola - Lecture notes in computer science, ECCV (14) 2020 cited by 823

  23. Learning Ordinal Relationships for Mid-Level Vision

    Authors: Daniel Zoran, Phillip Isola, Dilip Krishnan, William T. Freeman - IEEE International Conference on Computer Vision (ICCV) 2015 cited by 175

  24. A simple, efficient and scalable contrastive masked autoencoder for learning visual representations

    Authors: Shlok Mishra, Joshua Robinson, Huiwen Chang, David Jacobs, Aaron Sarna, Aaron Maschinot, Dilip Krishnan - arXiv (Cornell University), CoRR 2022 cited by 25