TAGLETS: A System for Automatic Semi-Supervised Learning with Auxiliary Data

TL;DR

TAGLETS is an open-source system that automatically combines labeled, unlabeled, and auxiliary data using knowledge graphs and ensemble methods to improve image classification performance beyond existing transfer and semi-supervised learning techniques.

Contribution

The paper introduces TAGLETS, a novel system that integrates multiple data types and learning modules with knowledge graphs and distillation for high-quality classifiers.

Findings

TAGLETS often outperforms state-of-the-art methods.

Incorporating auxiliary data improves classification accuracy.

Performance varies with data relatedness and labeled data amount.

Abstract

Machine learning practitioners often have access to a spectrum of data: labeled data for the target task (which is often limited), unlabeled data, and auxiliary data, the many available labeled datasets for other tasks. We describe TAGLETS, a system built to study techniques for automatically exploiting all three types of data and creating high-quality, servable classifiers. The key components of TAGLETS are: (1) auxiliary data organized according to a knowledge graph, (2) modules encapsulating different methods for exploiting auxiliary and unlabeled data, and (3) a distillation stage in which the ensembled modules are combined into a servable model. We compare TAGLETS with state-of-the-art transfer learning and semi-supervised learning methods on four image classification tasks. Our study covers a range of settings, varying the amount of labeled data and the semantic relatedness of the auxiliary data to the target task. We find that the intelligent incorporation of auxiliary and unlabeled data into multiple learning techniques enables TAGLETS to match-and most often significantly surpass-these alternatives. TAGLETS is available as an open-source system at github.com/BatsResearch/taglets.