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Dilip Krishnan
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2020 – today
- 2024
- [c37]Lijie Fan, Kaifeng Chen, Dilip Krishnan, Dina Katabi, Phillip Isola, Yonglong Tian:
Scaling Laws of Synthetic Images for Model Training ... for Now. CVPR 2024: 7382-7392 - [c36]Yonglong Tian, Lijie Fan, Kaifeng Chen, Dina Katabi, Dilip Krishnan, Phillip Isola:
Learning Vision from Models Rivals Learning Vision from Data. CVPR 2024: 15887-15898 - [c35]Tianhong Li, Sangnie Bhardwaj, Yonglong Tian, Han Zhang, Jarred Barber, Dina Katabi, Guillaume Lajoie, Huiwen Chang, Dilip Krishnan:
Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency. ICLR 2024 - 2023
- [j10]Apurva Kalia, Dilip Krishnan, Soha Hassoun:
CSI: Contrastive data Stratification for Interaction prediction and its application to compound-protein interaction prediction. Bioinform. 39(8) (2023) - [c34]Tianhong Li, Huiwen Chang, Shlok Kumar Mishra, Han Zhang, Dina Katabi, Dilip Krishnan:
MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis. CVPR 2023: 2142-2152 - [c33]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:
Muse: Text-To-Image Generation via Masked Generative Transformers. ICML 2023: 4055-4075 - [c32]Lijie Fan, Dilip Krishnan, Phillip Isola, Dina Katabi, Yonglong Tian:
Improving CLIP Training with Language Rewrites. NeurIPS 2023 - [c31]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:
StyleDrop: Text-to-Image Synthesis of Any Style. NeurIPS 2023 - [c30]Yonglong Tian, Lijie Fan, Phillip Isola, Huiwen Chang, Dilip Krishnan:
StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners. NeurIPS 2023 - [i34]Huiwen Chang, Han Zhang, Jarred Barber, Aaron Maschinot, José Lezama, Lu Jiang, Ming-Hsuan Yang, Kevin Murphy, William T. Freeman, Michael Rubinstein, Yuanzhen Li, Dilip Krishnan:
Muse: Text-To-Image Generation via Masked Generative Transformers. CoRR abs/2301.00704 (2023) - [i33]Sangnie Bhardwaj, Willie McClinton, Tongzhou Wang, Guillaume Lajoie, Chen Sun, Phillip Isola, Dilip Krishnan:
Steerable Equivariant Representation Learning. CoRR abs/2302.11349 (2023) - [i32]Lijie Fan, Dilip Krishnan, Phillip Isola, Dina Katabi, Yonglong Tian:
Improving CLIP Training with Language Rewrites. CoRR abs/2305.20088 (2023) - [i31]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:
StyleDrop: Text-to-Image Generation in Any Style. CoRR abs/2306.00983 (2023) - [i30]Yonglong Tian, Lijie Fan, Phillip Isola, Huiwen Chang, Dilip Krishnan:
StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners. CoRR abs/2306.00984 (2023) - [i29]Cyrus Rashtchian, Charles Herrmann, Chun-Sung Ferng, Ayan Chakrabarti, Dilip Krishnan, Deqing Sun, Da-Cheng Juan, Andrew Tomkins:
Substance or Style: What Does Your Image Embedding Know? CoRR abs/2307.05610 (2023) - [i28]Tianhong Li, Sangnie Bhardwaj, Yonglong Tian, Han Zhang, Jarred Barber, Dina Katabi, Guillaume Lajoie, Huiwen Chang, Dilip Krishnan:
Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency. CoRR abs/2310.03734 (2023) - [i27]Kaifeng Chen, Daniel Salz, Huiwen Chang, Kihyuk Sohn, Dilip Krishnan, Mojtaba Seyedhosseini:
Improve Supervised Representation Learning with Masked Image Modeling. CoRR abs/2312.00950 (2023) - [i26]Lijie Fan, Kaifeng Chen, Dilip Krishnan, Dina Katabi, Phillip Isola, Yonglong Tian:
Scaling Laws of Synthetic Images for Model Training ... for Now. CoRR abs/2312.04567 (2023) - [i25]Yonglong Tian, Lijie Fan, Kaifeng Chen, Dina Katabi, Dilip Krishnan, Phillip Isola:
Learning Vision from Models Rivals Learning Vision from Data. CoRR abs/2312.17742 (2023) - 2022
- [j9]Shlok Kumar Mishra, Anshul Shah, Ankan Bansal, Janit Anjaria, Abhyuday N. Jagannatha, Abhishek Sharma, David Jacobs, Dilip Krishnan:
Object-aware Cropping for Self-Supervised Learning. Trans. Mach. Learn. Res. 2022 (2022) - [c29]Charles Herrmann, Kyle Sargent, Lu Jiang, Ramin Zabih, Huiwen Chang, Ce Liu, Dilip Krishnan, Deqing Sun:
Pyramid Adversarial Training Improves ViT Performance. CVPR 2022: 13409-13419 - [i24]Shlok Mishra, Joshua Robinson, Huiwen Chang, David Jacobs, Aaron Sarna, Aaron Maschinot, Dilip Krishnan:
A simple, efficient and scalable contrastive masked autoencoder for learning visual representations. CoRR abs/2210.16870 (2022) - [i23]Tianhong Li, Huiwen Chang, Shlok Kumar Mishra, Han Zhang, Dina Katabi, Dilip Krishnan:
MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis. CoRR abs/2211.09117 (2022) - 2021
- [c28]Piotr Teterwak, Chiyuan Zhang, Dilip Krishnan, Michael C. Mozer:
Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers. ICML 2021: 10225-10235 - [i22]Piotr Teterwak, Chiyuan Zhang, Dilip Krishnan, Michael C. Mozer:
Understanding invariance via feedforward inversion of discriminatively trained classifiers. CoRR abs/2103.07470 (2021) - [i21]Andrea Burns, Aaron Sarna, Dilip Krishnan, Aaron Maschinot:
Unsupervised Disentanglement without Autoencoding: Pitfalls and Future Directions. CoRR abs/2108.06613 (2021) - [i20]Apurva Kalia, Dilip Krishnan, Soha Hassoun:
Contrastive Multiview Coding for Enzyme-Substrate Interaction Prediction. CoRR abs/2111.09467 (2021) - [i19]Charles Herrmann, Kyle Sargent, Lu Jiang, Ramin Zabih, Huiwen Chang, Ce Liu, Dilip Krishnan, Deqing Sun:
Pyramid Adversarial Training Improves ViT Performance. CoRR abs/2111.15121 (2021) - [i18]Shlok Mishra, Anshul Shah, Ankan Bansal, Abhyuday Jagannatha, Abhishek Sharma, David Jacobs, Dilip Krishnan:
Object-Aware Cropping for Self-Supervised Learning. CoRR abs/2112.00319 (2021) - 2020
- [c27]Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B. Tenenbaum, Phillip Isola:
Rethinking Few-Shot Image Classification: A Good Embedding is All You Need? ECCV (14) 2020: 266-282 - [c26]Yonglong Tian, Dilip Krishnan, Phillip Isola:
Contrastive Multiview Coding. ECCV (11) 2020: 776-794 - [c25]Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, Samy Bengio:
Fantastic Generalization Measures and Where to Find Them. ICLR 2020 - [c24]Yonglong Tian, Dilip Krishnan, Phillip Isola:
Contrastive Representation Distillation. ICLR 2020 - [c23]Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, Dilip Krishnan:
Supervised Contrastive Learning. NeurIPS 2020 - [c22]Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, Phillip Isola:
What Makes for Good Views for Contrastive Learning? NeurIPS 2020 - [i17]Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B. Tenenbaum, Phillip Isola:
Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need? CoRR abs/2003.11539 (2020) - [i16]Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, Dilip Krishnan:
Supervised Contrastive Learning. CoRR abs/2004.11362 (2020) - [i15]Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, Phillip Isola:
What makes for good views for contrastive learning. CoRR abs/2005.10243 (2020)
2010 – 2019
- 2019
- [c21]Dilip Krishnan, Piotr Teterwak, Aaron Sarna, Aaron Maschinot, Ce Liu, David Belanger, William T. Freeman:
Boundless: Generative Adversarial Networks for Image Extension. ICCV 2019: 10520-10529 - [c20]Yiding Jiang, Dilip Krishnan, Hossein Mobahi, Samy Bengio:
Predicting the Generalization Gap in Deep Networks with Margin Distributions. ICLR (Poster) 2019 - [c19]Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy Dvijotham, Alhussein Fawzi, Soham De, Robert Stanforth, Pushmeet Kohli:
Adversarial Robustness through Local Linearization. NeurIPS 2019: 13824-13833 - [i14]Vighnesh Birodkar, Hossein Mobahi, Dilip Krishnan, Samy Bengio:
A Closed-Form Learned Pooling for Deep Classification Networks. CoRR abs/1906.03808 (2019) - [i13]Yonglong Tian, Dilip Krishnan, Phillip Isola:
Contrastive Multiview Coding. CoRR abs/1906.05849 (2019) - [i12]Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy Dvijotham, Alhussein Fawzi, Soham De, Robert Stanforth, Pushmeet Kohli:
Adversarial Robustness through Local Linearization. CoRR abs/1907.02610 (2019) - [i11]Piotr Teterwak, Aaron Sarna, Dilip Krishnan, Aaron Maschinot, David Belanger, Ce Liu, William T. Freeman:
Boundless: Generative Adversarial Networks for Image Extension. CoRR abs/1908.07007 (2019) - [i10]Yonglong Tian, Dilip Krishnan, Phillip Isola:
Contrastive Representation Distillation. CoRR abs/1910.10699 (2019) - [i9]Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, Samy Bengio:
Fantastic Generalization Measures and Where to Find Them. CoRR abs/1912.02178 (2019) - 2018
- [j8]Kanit Wongsuphasawat, Daniel Smilkov, James Wexler, Jimbo Wilson, Dandelion Mané, Doug Fritz, Dilip Krishnan, Fernanda B. Viégas, Martin Wattenberg:
Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow. IEEE Trans. Vis. Comput. Graph. 24(1): 1-12 (2018) - [c18]Tali Dekel, Chuang Gan, Dilip Krishnan, Ce Liu, William T. Freeman:
Sparse, Smart Contours to Represent and Edit Images. CVPR 2018: 3511-3520 - [c17]Gamaleldin F. Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, Samy Bengio:
Large Margin Deep Networks for Classification. NeurIPS 2018: 850-860 - [i8]Gamaleldin F. Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, Samy Bengio:
Large Margin Deep Networks for Classification. CoRR abs/1803.05598 (2018) - [i7]Yiding Jiang, Dilip Krishnan, Hossein Mobahi, Samy Bengio:
Predicting the Generalization Gap in Deep Networks with Margin Distributions. CoRR abs/1810.00113 (2018) - 2017
- [c16]Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, Dilip Krishnan:
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks. CVPR 2017: 95-104 - [c15]Forrester Cole, David Belanger, Dilip Krishnan, Aaron Sarna, Inbar Mosseri, William T. Freeman:
Synthesizing Normalized Faces from Facial Identity Features. CVPR 2017: 3386-3395 - [c14]Yaniv Ovadia, Yoni Halpern, Dilip Krishnan, Josh Livni, Daniel E. Newburger, Ryan Poplin, Tiantian Zha, D. Sculley:
Learning to Count Mosquitoes for the Sterile Insect Technique. KDD 2017: 1943-1949 - [i6]Forrester Cole, David Belanger, Dilip Krishnan, Aaron Sarna, Inbar Mosseri, William T. Freeman:
Face Synthesis from Facial Identity Features. CoRR abs/1701.04851 (2017) - [i5]Tali Dekel, Chuang Gan, Dilip Krishnan, Ce Liu, William T. Freeman:
Smart, Sparse Contours to Represent and Edit Images. CoRR abs/1712.08232 (2017) - 2016
- [c13]Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, Dumitru Erhan:
Domain Separation Networks. NIPS 2016: 343-351 - [i4]Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, Dumitru Erhan:
Domain Separation Networks. CoRR abs/1608.06019 (2016) - [i3]Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, Dilip Krishnan:
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks. CoRR abs/1612.05424 (2016) - 2015
- [c12]Yichang Shih, Dilip Krishnan, Frédo Durand, William T. Freeman:
Reflection removal using ghosting cues. CVPR 2015: 3193-3201 - [c11]Daniel Zoran, Phillip Isola, Dilip Krishnan, William T. Freeman:
Learning Ordinal Relationships for Mid-Level Vision. ICCV 2015: 388-396 - [i2]Phillip Isola, Daniel Zoran, Dilip Krishnan, Edward H. Adelson:
Learning visual groups from co-occurrences in space and time. CoRR abs/1511.06811 (2015) - 2014
- [c10]Phillip Isola, Daniel Zoran, Dilip Krishnan, Edward H. Adelson:
Crisp Boundary Detection Using Pointwise Mutual Information. ECCV (3) 2014: 799-814 - [c9]Daniel Zoran, Dilip Krishnan, José Bento, Bill Freeman:
Shape and Illumination from Shading using the Generic Viewpoint Assumption. NIPS 2014: 226-234 - 2013
- [b1]Dilip Krishnan:
Low-level Image Priors and Laplacian Preconditioners for Applications in Computer Graphics and Computational Photography. New York University, USA, 2013 - [j7]Dilip Krishnan, Raanan Fattal, Richard Szeliski:
Efficient preconditioning of laplacian matrices for computer graphics. ACM Trans. Graph. 32(4): 142:1-142:15 (2013) - [c8]David Eigen, Dilip Krishnan, Rob Fergus:
Restoring an Image Taken through a Window Covered with Dirt or Rain. ICCV 2013: 633-640 - [i1]Dilip Krishnan, Joan Bruna, Rob Fergus:
Blind Deconvolution with Re-weighted Sparsity Promotion. CoRR abs/1311.4029 (2013) - 2011
- [j6]Dilip Krishnan, Richard Szeliski:
Multigrid and multilevel preconditioners for computational photography. ACM Trans. Graph. 30(6): 177 (2011) - [c7]Dilip Krishnan, Terence Tay, Rob Fergus:
Blind deconvolution using a normalized sparsity measure. CVPR 2011: 233-240 - 2010
- [c6]Matthew D. Zeiler, Dilip Krishnan, Graham W. Taylor, Robert Fergus:
Deconvolutional networks. CVPR 2010: 2528-2535
2000 – 2009
- 2009
- [j5]Dilip Krishnan, Quang Vinh Pham, Andy M. Yip:
A primal-dual active-set algorithm for bilaterally constrained total variation deblurring and piecewise constant Mumford-Shah segmentation problems. Adv. Comput. Math. 31(1-3): 237-266 (2009) - [j4]Dilip Krishnan, Rob Fergus:
Dark flash photography. ACM Trans. Graph. 28(3): 96 (2009) - [c5]Dilip Krishnan, Rob Fergus:
Fast Image Deconvolution using Hyper-Laplacian Priors. NIPS 2009: 1033-1041 - 2007
- [j3]Dilip Krishnan, Ping Lin, Andy M. Yip:
A Primal-Dual Active-Set Method for Non-Negativity Constrained Total Variation Deblurring Problems. IEEE Trans. Image Process. 16(11): 2766-2777 (2007)
1990 – 1999
- 1999
- [j2]Dilip Krishnan, Man-Nang Chong, Showbhik Kalra:
On the computational aspects of Gibbs-Markov random field modeling of missing-data in image sequences. IEEE Trans. Image Process. 8(8): 1139-1142 (1999) - [c4]Showbhik Kalra, Dilip Krishnan, Man-Nang Chong:
A MRF Model Based Scheme for Accurate Detection and Adaptive Interpolation of Missing Data in Highly Corrupted Image Sequences. ICIP (2) 1999: 890-893 - 1998
- [j1]Man-Nang Chong, Dilip Krishnan:
An edge-preserving MRF model for the detection of missing data in image sequences. IEEE Signal Process. Lett. 5(4): 81-83 (1998) - 1997
- [c3]Showbhik Kalra, Man-Nang Chong, Dilip Krishnan:
A new auto-regressive (AR) model-based algorithm for motion picture restoration. ICASSP 1997: 2557-2560 - [c2]Man-Nang Chong, P. Liu, Wooi Boon Goh, Dilip Krishnan:
A new spatio-temporal MRF model for the detection of missing data in image sequences. ICASSP 1997: 2977-2980 - 1996
- [c1]Wooi Boon Goh, Man-Nang Chong, Showbhik Kalra, Dilip Krishnan:
Bi-directional 3D auto-regressive model approach to motion picture restoration. ICASSP 1996: 2275-2278
Coauthor Index
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last updated on 2024-10-07 01:24 CEST by the dblp team
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