Dilip Krishnan

According to our database, Dilip Krishnan authored at least 61 papers between 1996 and 2023.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2023

CSI: Contrastive data Stratification for Interaction prediction and its application to compound-protein interaction prediction.

Apurva Kalia, Dilip Krishnan, Soha Hassoun

Bioinform., August, 2023

Learning Vision from Models Rivals Learning Vision from Data.

Yonglong Tian, Lijie Fan, Kaifeng Chen, Dina Katabi, Dilip Krishnan, Phillip Isola

CoRR, 2023

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

Lijie Fan, Kaifeng Chen, Dilip Krishnan, Dina Katabi, Phillip Isola, Yonglong Tian

CoRR, 2023

Improve Supervised Representation Learning with Masked Image Modeling.

Kaifeng Chen, Daniel Salz, Huiwen Chang, Kihyuk Sohn, Dilip Krishnan, Mojtaba Seyedhosseini

CoRR, 2023

Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency.

Tianhong Li, Sangnie Bhardwaj, Yonglong Tian, Han Zhang, Jarred Barber, Dina Katabi, Guillaume Lajoie, Huiwen Chang, Dilip Krishnan

CoRR, 2023

Substance or Style: What Does Your Image Embedding Know?

Cyrus Rashtchian, Charles Herrmann, Chun-Sung Ferng, Ayan Chakrabarti, Dilip Krishnan, Deqing Sun, Da-Cheng Juan, Andrew Tomkins

CoRR, 2023

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

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

CoRR, 2023

Steerable Equivariant Representation Learning.

Sangnie Bhardwaj, Willie McClinton, Tongzhou Wang, Guillaume Lajoie, Chen Sun, Phillip Isola, Dilip Krishnan

CoRR, 2023

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

Yonglong Tian, Lijie Fan, Phillip Isola, Huiwen Chang, Dilip Krishnan

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

StyleDrop: Text-to-Image Synthesis of Any Style.

Kihyuk Sohn, Lu Jiang, Jarred Barber, Kimin Lee, Nataniel Ruiz, Dilip Krishnan, Huiwen Chang, Yuanzhen Li, Irfan Essa, Michael Rubinstein, Yuan Hao, Glenn Entis, Irina Blok, Daniel Castro Chin

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Improving CLIP Training with Language Rewrites.

Lijie Fan, Dilip Krishnan, Phillip Isola, Dina Katabi, Yonglong Tian

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

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

Proceedings of the International Conference on Machine Learning, 2023

MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis.

Tianhong Li, Huiwen Chang, Shlok Kumar Mishra, Han Zhang, Dina Katabi, Dilip Krishnan

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Object-aware Cropping for Self-Supervised Learning.

Shlok Kumar Mishra, Anshul Shah, Ankan Bansal, Janit Anjaria, Abhyuday N. Jagannatha, Abhishek Sharma, David Jacobs, Dilip Krishnan

Trans. Mach. Learn. Res., 2022

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

Shlok Mishra, Joshua Robinson, Huiwen Chang, David Jacobs, Aaron Sarna, Aaron Maschinot, Dilip Krishnan

CoRR, 2022

Pyramid Adversarial Training Improves ViT Performance.

Charles Herrmann, Kyle Sargent, Lu Jiang, Ramin Zabih, Huiwen Chang, Ce Liu, Dilip Krishnan, Deqing Sun

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

Object-Aware Cropping for Self-Supervised Learning.

Shlok Mishra, Anshul Shah, Ankan Bansal, Abhyuday Jagannatha, Abhishek Sharma, David Jacobs, Dilip Krishnan

CoRR, 2021

Contrastive Multiview Coding for Enzyme-Substrate Interaction Prediction.

Apurva Kalia, Dilip Krishnan, Soha Hassoun

CoRR, 2021

Unsupervised Disentanglement without Autoencoding: Pitfalls and Future Directions.

Andrea Burns, Aaron Sarna, Dilip Krishnan, Aaron Maschinot

CoRR, 2021

Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers.

Piotr Teterwak, Chiyuan Zhang, Dilip Krishnan, Michael C. Mozer

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

What Makes for Good Views for Contrastive Learning?

Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, Phillip Isola

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Supervised Contrastive Learning.

Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, Dilip Krishnan

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Contrastive Representation Distillation.

Yonglong Tian, Dilip Krishnan, Phillip Isola

Proceedings of the 8th International Conference on Learning Representations, 2020

Fantastic Generalization Measures and Where to Find Them.

Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, Samy Bengio

Proceedings of the 8th International Conference on Learning Representations, 2020

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

Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B. Tenenbaum, Phillip Isola

Proceedings of the Computer Vision - ECCV 2020, 2020

Contrastive Multiview Coding.

Yonglong Tian, Dilip Krishnan, Phillip Isola

Proceedings of the Computer Vision - ECCV 2020, 2020

2019

A Closed-Form Learned Pooling for Deep Classification Networks.

Vighnesh Birodkar, Hossein Mobahi, Dilip Krishnan, Samy Bengio

CoRR, 2019

Adversarial Robustness through Local Linearization.

Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy Dvijotham, Alhussein Fawzi, Soham De, Robert Stanforth, Pushmeet Kohli

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Predicting the Generalization Gap in Deep Networks with Margin Distributions.

Yiding Jiang, Dilip Krishnan, Hossein Mobahi, Samy Bengio

Proceedings of the 7th International Conference on Learning Representations, 2019

Boundless: Generative Adversarial Networks for Image Extension.

Dilip Krishnan, Piotr Teterwak, Aaron Sarna, Aaron Maschinot, Ce Liu, David Belanger, William T. Freeman

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

2018

Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow.

Kanit Wongsuphasawat, Daniel Smilkov, James Wexler, Jimbo Wilson, Dandelion Mané, Doug Fritz, Dilip Krishnan, Fernanda B. Viégas, Martin Wattenberg

IEEE Trans. Vis. Comput. Graph., 2018

Large Margin Deep Networks for Classification.

Gamaleldin F. Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, Samy Bengio

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Sparse, Smart Contours to Represent and Edit Images.

Tali Dekel, Chuang Gan, Dilip Krishnan, Ce Liu, William T. Freeman

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2017

Smart, Sparse Contours to Represent and Edit Images.

Tali Dekel, Chuang Gan, Dilip Krishnan, Ce Liu, William T. Freeman

CoRR, 2017

Face Synthesis from Facial Identity Features.

Forrester Cole, David Belanger, Dilip Krishnan, Aaron Sarna, Inbar Mosseri, William T. Freeman

CoRR, 2017

Learning to Count Mosquitoes for the Sterile Insect Technique.

Yaniv Ovadia, Yoni Halpern, Dilip Krishnan, Josh Livni, Daniel E. Newburger, Ryan Poplin, Tiantian Zha, D. Sculley

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

Synthesizing Normalized Faces from Facial Identity Features.

Forrester Cole, David Belanger, Dilip Krishnan, Aaron Sarna, Inbar Mosseri, William T. Freeman

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks.

Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, Dilip Krishnan

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

2016

Domain Separation Networks.

Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, Dumitru Erhan

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Learning visual groups from co-occurrences in space and time.

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

CoRR, 2015

Learning Ordinal Relationships for Mid-Level Vision.

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

Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015

Reflection removal using ghosting cues.

Yichang Shih, Dilip Krishnan, Frédo Durand, William T. Freeman

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

2014

Shape and Illumination from Shading using the Generic Viewpoint Assumption.

Daniel Zoran, Dilip Krishnan, José Bento, Bill Freeman

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Crisp Boundary Detection Using Pointwise Mutual Information.

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

Proceedings of the Computer Vision - ECCV 2014, 2014

2013

Low-level Image Priors and Laplacian Preconditioners for Applications in Computer Graphics and Computational Photography.

Dilip Krishnan

PhD thesis, 2013

Efficient preconditioning of laplacian matrices for computer graphics.

Dilip Krishnan, Raanan Fattal, Richard Szeliski

ACM Trans. Graph., 2013

Blind Deconvolution with Re-weighted Sparsity Promotion.

Dilip Krishnan, Joan Bruna, Rob Fergus

CoRR, 2013

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

David Eigen, Dilip Krishnan, Rob Fergus

Proceedings of the IEEE International Conference on Computer Vision, 2013

2011

Multigrid and multilevel preconditioners for computational photography.

Dilip Krishnan, Richard Szeliski

ACM Trans. Graph., 2011

Blind deconvolution using a normalized sparsity measure.

Dilip Krishnan, Terence Tay, Rob Fergus

Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition, 2011

2010

Deconvolutional networks.

Matthew D. Zeiler, Dilip Krishnan, Graham W. Taylor, Robert Fergus

Proceedings of the Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, 2010

2009

Dark flash photography.

Dilip Krishnan, Rob Fergus

ACM Trans. Graph., 2009

A primal-dual active-set algorithm for bilaterally constrained total variation deblurring and piecewise constant Mumford-Shah segmentation problems.

Dilip Krishnan, Quang Vinh Pham, Andy M. Yip

Adv. Comput. Math., 2009

Fast Image Deconvolution using Hyper-Laplacian Priors.

Dilip Krishnan, Rob Fergus

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2007

A Primal-Dual Active-Set Method for Non-Negativity Constrained Total Variation Deblurring Problems.

Dilip Krishnan, Ping Lin, Andy M. Yip

IEEE Trans. Image Process., 2007

1999

On the computational aspects of Gibbs-Markov random field modeling of missing-data in image sequences.

Dilip Krishnan, Man-Nang Chong, Showbhik Kalra

IEEE Trans. Image Process., 1999

A MRF Model Based Scheme for Accurate Detection and Adaptive Interpolation of Missing Data in Highly Corrupted Image Sequences.

Showbhik Kalra, Dilip Krishnan, Man-Nang Chong

Proceedings of the 1999 International Conference on Image Processing, 1999

1998

An edge-preserving MRF model for the detection of missing data in image sequences.

Man-Nang Chong, Dilip Krishnan

IEEE Signal Process. Lett., 1998

1997

A new auto-regressive (AR) model-based algorithm for motion picture restoration.

Showbhik Kalra, Man-Nang Chong, Dilip Krishnan

Proceedings of the 1997 IEEE International Conference on Acoustics, 1997

A new spatio-temporal MRF model for the detection of missing data in image sequences.

Man-Nang Chong, P. Liu, Wooi Boon Goh, Dilip Krishnan

Proceedings of the 1997 IEEE International Conference on Acoustics, 1997

1996

Bi-directional 3D auto-regressive model approach to motion picture restoration.

Wooi Boon Goh, Man-Nang Chong, Showbhik Kalra, Dilip Krishnan

Proceedings of the 1996 IEEE International Conference on Acoustics, 1996