Supervised learning can improve the design of state-of-the-art solvers for
combinatorial problems, but labelling large numbers of combinatorial instances
is often impractical due to exponential worst-case complexity. Inspired by the
recent success of contrastive pre-training for images, we conduct a scientific
study of the effect of augmentation design on contrastive pre-training for the
Boolean satisfiability problem. While typical graph contrastive pre-training
uses label-agnostic augmentations, our key insight is that many combinatorial
problems have well-studied invariances, which allow for the design of
label-preserving augmentations. We find that label-preserving augmentations are
critical for the success of contrastive pre-training. We show that our
representations are able to achieve comparable test accuracy to
fully-supervised learning while using only 1% of the labels. We also
demonstrate that our representations are more transferable to larger problems
from unseen domains. Our code is available at
this https URL.

通过引入 label-preserving augmentations 和对 contrastive pre-training 进行科学研究，我们发现可以利用有标签的数据集中仅 1% 的数据就可以训练出与全监督学习具有可比性的表示方法，并且这些表示方法更易于从新领域中进行更大规模的推广应用。