We propose Guided Positive Sampling Self-Supervised Learning (GPS-SSL), a general method to inject a priori knowledge into Self-Supervised Learning (SSL) positive samples selection. Current SSL methods leverage Data-Augmentations (DA) for generating positive samples and incorporate prior knowledge - an incorrect, or too weak DA will drastically reduce the quality of the learned representation. GPS-SSL proposes instead to design a metric space where Euclidean distances become a meaningful proxy for semantic relationship. In that space, it is now possible to generate positive samples from nearest neighbor sampling. Any prior knowledge can now be embedded into that metric space independently from the employed DA. From its simplicity, GPS-SSL is applicable to any SSL method, e.g. SimCLR or BYOL. A key benefit of GPS-SSL is in reducing the pressure in tailoring strong DAs. For example GPS-SSL reaches 85.58% on Cifar10 with weak DA while the baseline only reaches 37.51%. We therefore move a step forward towards the goal of making SSL less reliant on DA. We also show that even when using strong DAs, GPS-SSL outperforms the baselines on under-studied domains. We evaluate GPS-SSL along with multiple baseline SSL methods on numerous downstream datasets from different domains when the models use strong or minimal data augmentations. We hope that GPS-SSL will open new avenues in studying how to inject a priori knowledge into SSL in a principled manner.

翻译：我们提出引导正采样自监督学习（GPS-SSL），这是一种将先验知识注入自监督学习（SSL）正样本选择的通用方法。当前SSL方法利用数据增强（DA）生成正样本，但若融入的先验知识不当或增强方式过弱，会显著降低所学表征的质量。GPS-SSL提出设计一个度量空间，在该空间中欧氏距离成为语义关系的有意义代理。在此空间内，可通过最近邻采样生成正样本。任何先验知识现在均可独立于所采用的数据增强嵌入该度量空间。凭借其简洁性，GPS-SSL可适用于任何SSL方法（如SimCLR或BYOL）。GPS-SSL的一个关键优势在于减轻了定制强数据增强的压力。例如，在Cifar10数据集上使用弱数据增强时，GPS-SSL达到85.58%的准确率，而基线方法仅达37.51%。因此，我们在使SSL减少对数据增强依赖的目标上迈出了一步。我们还证明，即使使用强数据增强，GPS-SSL在尚未充分研究的领域上也优于基线方法。我们评估了GPS-SSL与多种基线SSL方法在来自不同领域的多个下游数据集上的表现，这些模型使用了强或最小化的数据增强。我们希望GPS-SSL能开辟研究如何以原则性方式将先验知识注入SSL的新途径。