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Self-supervision through Random Segments with Autoregressive Coding (RandSAC)


Mar 22, 2022
Tianyu Hua, Yonglong Tian, Sucheng Ren, Hang Zhao, Leonid Sigal

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Co-advise: Cross Inductive Bias Distillation


Jun 23, 2021
Sucheng Ren, Zhengqi Gao, Tianyu Hua, Zihui Xue, Yonglong Tian, Shengfeng He, Hang Zhao

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Simple Distillation Baselines for Improving Small Self-supervised Models


Jun 21, 2021
Jindong Gu, Wei Liu, Yonglong Tian

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Generative Models as a Data Source for Multiview Representation Learning


Jun 09, 2021
Ali Jahanian, Xavier Puig, Yonglong Tian, Phillip Isola

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Divide and Contrast: Self-supervised Learning from Uncurated Data


May 17, 2021
Yonglong Tian, Olivier J. Henaff, Aaron van den Oord

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Composable Augmentation Encoding for Video Representation Learning


Apr 01, 2021
Chen Sun, Arsha Nagrani, Yonglong Tian, Cordelia Schmid

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Information-Preserving Contrastive Learning for Self-Supervised Representations


Dec 17, 2020
Tianhong Li, Lijie Fan, Yuan Yuan, Hao He, Yonglong Tian, Dina Katabi

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* The first two authors contributed equally to this paper 

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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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