Generating large labeled training data is becoming the biggest bottleneck in building and deploying supervised machine learning models. Recently, data programming has been proposed in the data management community to reduce the human cost in training data generation. Data programming expects users to write a set of labeling functions, each of which is a weak supervision source that labels a subset of data points with better-than-random accuracy. However, the success of data programming heavily depends on the quality (in terms of both accuracy and coverage) of the labeling functions that users still need to design manually. We propose affinity coding, a new paradigm for fully automatic generation of training data. In affinity coding, the similarity between the unlabeled instances and prototypes that are derived from the same unlabeled instances serve as signals (or sources of weak supervision) for determining class membership. We term this implicit similarity as the affinity score. Consequently, we can have as many sources of weak supervision as the number of unlabeled data points, without any human input. We also propose a system called GOGGLES that is an implementation of affinity coding for labeling image datasets. GOGGLES features novel techniques for deriving affinity scores from image datasets based on "semantic prototypes" extracted from convolutional neural nets, as well as an expectation-maximization approach for performing class label inference based on the computed affinity scores. Compared to the state-of-the-art data programming system Snorkel, GOGGLES exhibits 14.88% average improvement in terms of the quality of labels generated for the binary labeling task. The GOGGLES system is open-sourced at https://github.com/chu-data-lab/GOGGLES/.

本文提出了一种新的自动训练数据标注范例——亲和编码法，并设计了一种可重复利用的适用于图像数据的亲和函数集合；在五个不同领域的图像标注任务上与现有数据编程系统 Snuba 和新型的少样本学习技术进行了比较，结果显示，该系统精度在 71% 至 98% 之间，端到端表现优于现有系统，在不需要人工标注的情况下，比新型少样本学习技术和 Snuba 系统性能更优，非常接近完全受监督模型的上限。

GOGGLES：自动图像标注与相似性编码