  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: https://github.com/Lee-Gihun/MixCo-Mixup-Contrast.
