Contrastive learning has shown remarkable results in recent self-supervised
approaches for visual representation. By learning to contrast positive pairs'
representation from the corresponding negatives pairs, one can train good
visual representations without human annotations. This paper proposes Mix-up
Contrast (MixCo), which extends the contrastive learning concept to
semi-positives encoded from the mix-up of positive and negative images. MixCo
aims to learn the relative similarity of representations, reflecting how much
the mixed images have the original positives. We validate the efficacy of MixCo
when applied to the recent self-supervised learning algorithms under the
standard linear evaluation protocol on TinyImageNet, CIFAR10, and CIFAR100. In
the experiments, MixCo consistently improves test accuracy. Remarkably, the
improvement is more significant when the learning capacity (e.g., model size)
is limited, suggesting that MixCo might be more useful in real-world scenarios.
The code is available at: this https URL

本篇论文提出一种名为 MixCo 的半阳性对比学习方法，它可以通过学习混合图像的相关相似度，有效提高自我监督学习中的图像表示性能。在实验中，MixCo 在 TinyImageNet、CIFAR10 和 CIFAR100 等基准测试集上一致地提高了测试准确率，且当模型大小等学习能力受限时，改进效果更为显著。