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MoCo 是计算机视觉大牛何凯明组的工作,从 2019 年发表的 MoCo V1 到 2021 年的 MoCo V3 研究重心一直是基于对比学习的视觉表征模型学习,随着近几年视觉领域训练方法从有监督到无监督、模型骨架从 CNN 到 Transformer 的变化,MoCo 也不断地对方法进行了升级改造,持续推动自监督对比学习取得进展。本文将详解介绍 MoCo V1/V2/V3 方法,看作者是如何围绕一个问题不断给出优化解决方案的。