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Dilip Krishnan

Google Research

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


Nov 16, 2022
Tianhong Li, Huiwen Chang, Shlok Kumar Mishra, Han Zhang, Dina Katabi, Dilip Krishnan

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


Oct 30, 2022
Shlok Mishra, Joshua Robinson, Huiwen Chang, David Jacobs, Aaron Sarna, Aaron Maschinot, Dilip Krishnan

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* Mishra and Robinson contributed equally 

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Object-Aware Cropping for Self-Supervised Learning


Dec 01, 2021
Shlok Mishra, Anshul Shah, Ankan Bansal, Abhyuday Jagannatha, Abhishek Sharma, David Jacobs, Dilip Krishnan

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Pyramid Adversarial Training Improves ViT Performance


Nov 30, 2021
Charles Herrmann, Kyle Sargent, Lu Jiang, Ramin Zabih, Huiwen Chang, Ce Liu, Dilip Krishnan, Deqing Sun

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* 32 pages, including references & supplementary material 

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Contrastive Multiview Coding for Enzyme-Substrate Interaction Prediction


Nov 18, 2021
Apurva Kalia, Dilip Krishnan, Soha Hassoun

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* 3 pages, submitted to Machine Learning in Computational Biology 2021 

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Unsupervised Disentanglement without Autoencoding: Pitfalls and Future Directions


Aug 14, 2021
Andrea Burns, Aaron Sarna, Dilip Krishnan, Aaron Maschinot

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* Accepted at the ICML 2021 Self-Supervised Learning for Reasoning and Perception Workshop 

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Understanding invariance via feedforward inversion of discriminatively trained classifiers


Mar 15, 2021
Piotr Teterwak, Chiyuan Zhang, Dilip Krishnan, Michael C. Mozer

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What makes for good views for contrastive learning


May 20, 2020
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, Phillip Isola

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* submitted to ECCV 2020 

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Supervised Contrastive Learning


Apr 23, 2020
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, Dilip Krishnan

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Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?


Mar 25, 2020
Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B. Tenenbaum, Phillip Isola

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* First two authors contributed equally. Code: http://github.com/WangYueFt/rfs/ 

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