论文标题
通过动量对比学习改进基线
Improved Baselines with Momentum Contrastive Learning
论文作者
论文摘要
对比无监督的学习最近表现出令人鼓舞的进步，例如，在动量对比（MOCO）和SIMCLR中。在本说明中，我们通过在MOCO框架中实现SIMCLR的两个设计改进的有效性。通过简单地修改MOCO ---即使用MLP投影头和更多数据增强 - 我们建立了更强的基础线，以优于SIMCLR，并且不需要大量的培训批次。我们希望这将使最新的无监督学习研究更容易获得。代码将公开。
Contrastive unsupervised learning has recently shown encouraging progress, e.g., in Momentum Contrast (MoCo) and SimCLR. In this note, we verify the effectiveness of two of SimCLR's design improvements by implementing them in the MoCo framework. With simple modifications to MoCo---namely, using an MLP projection head and more data augmentation---we establish stronger baselines that outperform SimCLR and do not require large training batches. We hope this will make state-of-the-art unsupervised learning research more accessible. Code will be made public.
