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Contrastive learning has shown remarkable results in recent self-supervisedapproaches for visual representation. By learning to contrast positive pairs'representation from the corresponding negatives pairs, one can train goodvisual representations without human annotations. This paper proposes Mix-upContrast (MixCo), which extends the contrastive learning concept tosemi-positives encoded from the mix-up of positive and negative images. MixCoaims to learn the relative similarity of representations, reflecting how muchthe mixed images have the original positives. We validate the efficacy of MixCowhen applied to the recent self-supervised learning algorithms under thestandard linear evaluation protocol on TinyImageNet, CIFAR10, and CIFAR100. Inthe experiments, MixCo consistently improves test accuracy. Remarkably, theimprovement 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.

MixCo: Mix-up Contrastive Learning for Visual Representation