{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-simple-data-mixing-prior-for-improving-self-1","title":"A Simple Data Mixing Prior for Improving Self-Supervised Learning","arxiv_id":"2206.07692","date":"2022-06-15","proceeding":"CVPR 2022 1","authors":["Sucheng Ren","Huiyu Wang","Zhengqi Gao","Shengfeng He","Alan Yuille","Yuyin Zhou","Cihang Xie"],"abstract":"Data mixing (e.g., Mixup, Cutmix, ResizeMix) is an essential component for advancing recognition models. In this paper, we focus on studying its effectiveness in the self-supervised setting. By noticing the mixed images that share the same source images are intrinsically related to each other, we hereby propose SDMP, short for $\\textbf{S}$imple $\\textbf{D}$ata $\\textbf{M}$ixing $\\textbf{P}$rior, to capture this straightforward yet essential prior, and position such mixed images as additional $\\textbf{positive pairs}$ to facilitate self-supervised representation learning. Our experiments verify that the proposed SDMP enables data mixing to help a set of self-supervised learning frameworks (e.g., MoCo) achieve better accuracy and out-of-distribution robustness. More notably, our SDMP is the first method that successfully leverages data mixing to improve (rather than hurt) the performance of Vision Transformers in the self-supervised setting. 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