World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
17624
World Ranking
10435
National Ranking
4356

Overview

Dilip Krishnan is affiliated with Google in the United States, where their research primarily focuses on computer science. Their work encompasses multiple subfields, including computer vision and pattern recognition, artificial intelligence, molecular biology, radiology, nuclear medicine, imaging, as well as electrical and electronic engineering.

Their research topics cover a range of areas in machine learning and computer vision with emphasis on:

  • Domain Adaptation and Few-Shot Learning
  • Multimodal Machine Learning Applications
  • Advanced Neural Network Applications
  • Advanced Image and Video Retrieval Techniques
  • Generative Adversarial Networks and Image Synthesis
  • Adversarial Robustness in Machine Learning
  • Computational Drug Discovery Methods

Several recent papers co-authored by Dilip Krishnan illustrate their research focus and collaboration with other scholars. These include:

  • What Makes for Good Views for Contrastive Learning?, 2020, arXiv (Cornell University)
  • Muse: Text-To-Image Generation via Masked Generative Transformers, 2023, arXiv (Cornell University)
  • Supervised Contrastive Learning, 2020, arXiv (Cornell University)
  • Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?, 2020, arXiv (Cornell University)
  • Simplified Transfer Learning for Chest Radiography Models Using Less Data, 2022, Radiology

Dilip Krishnan frequently collaborates with several researchers, with notable coauthors including Yonglong Tian, Phillip Isola, Huiwen Chang, Dina Katabi, and Aaron Sarna. Their publication record is principally concentrated in venues such as arXiv (Cornell University), with 20 publications, followed by contributions to Bioinformatics, Radiology, the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), and SSRN Electronic Journal.

Best Publications

  • Contrastive Multiview Coding

    Yonglong Tian;Dilip Krishnan;Phillip Isola

  • Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks

    Konstantinos Bousmalis;Nathan Silberman;David Dohan;Dumitru Erhan

  • Deconvolutional networks

    Matthew D. Zeiler;Dilip Krishnan;Graham W. Taylor;Rob Fergus

  • Fast Image Deconvolution using Hyper-Laplacian Priors

    Dilip Krishnan;Rob Fergus

  • Blind deconvolution using a normalized sparsity measure

    Dilip Krishnan;Terence Tay;Rob Fergus

  • Domain separation networks

    Konstantinos Bousmalis;George Trigeorgis;Nathan Silberman;Dilip Krishnan

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

    Yonglong Tian;Yue Wang;Dilip Krishnan;Joshua B. Tenenbaum

  • What Makes for Good Views for Contrastive Learning

    Yonglong Tian;Chen Sun;Ben Poole;Dilip Krishnan

  • Contrastive Representation Distillation

    Yonglong Tian;Dilip Krishnan;Phillip Isola

  • Supervised Contrastive Learning

    Prannay Khosla;Piotr Teterwak;Chen Wang;Aaron Sarna

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

    David Eigen;Dilip Krishnan;Rob Fergus

  • Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow

    Kanit Wongsuphasawat;Daniel Smilkov;James Wexler;Jimbo Wilson

  • Reflection removal using ghosting cues

    YiChang Shih;Dilip Krishnan;Fredo Durand;William T. Freeman

  • Fantastic Generalization Measures and Where to Find Them

    Yiding Jiang;Behnam Neyshabur;Hossein Mobahi;Dilip Krishnan

  • Crisp Boundary Detection Using Pointwise Mutual Information

    Phillip Isola;Daniel Zoran;Dilip Krishnan;Edward H. Adelson

  • Adversarial Robustness through Local Linearization

    Chongli Qin;James Martens;Sven Gowal;Dilip Krishnan

  • Learning Ordinal Relationships for Mid-Level Vision

    Daniel Zoran;Phillip Isola;Dilip Krishnan;William T. Freeman

  • Synthesizing Normalized Faces from Facial Identity Features

    Forrester Cole;David Belanger;Dilip Krishnan;Aaron Sarna

  • Dark flash photography

    Dilip Krishnan;Rob Fergus

  • Fantastic Generalization Measures and Where to Find Them

    Yiding Jiang;Behnam Neyshabur;Dilip Krishnan;Hossein Mobahi

Frequent Co-Authors

Rob Fergus
Rob Fergus New York University
Samy Bengio
Samy Bengio Apple (United States)
Ce Liu
Ce Liu Microsoft (United States)
Dumitru Erhan
Dumitru Erhan Google (United States)
Chen Sun
Chen Sun Google (United States)
Ben Poole
Ben Poole Google (United States)
Cordelia Schmid
Cordelia Schmid French Institute for Research in Computer Science and Automation - INRIA
Chuang Gan
Chuang Gan University of Massachusetts Amherst

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online education can open up new opportunities for aspiring computer science students. Many cheapest online colleges in the USA provide affordable and flexible programs, helping to reduce financial barriers and student debt. With various options to study from anywhere, more students can now access quality education at their own pace.

Worried about your academic history? You might be interested in online colleges that accept low gpa. These inclusive programs consider factors beyond grades, giving a wider range of learners the chance to pursue a computer science degree.

For those looking for faster ways to enter the tech workforce, online computer science degree programs with an accelerated format are available. These allow motivated students to earn their degree in less time and quickly start their careers.

Computer science graduates can also apply their skills to diverse fields. For example, understanding technology can complement careers in sustainability and analytics. If you’re curious about other fields, check out what you can do with an environmental science degree — many modern careers blend multiple disciplines for broader impact.

Best Scientists Citing Dilip Krishnan

Trending Scientists