# Shoestring: Graph-Based Semi-Supervised Learning with Severely Limited Labeled Data

Source: [Shoestring: Graph-Based Semi-Supervised Learning with Severely Limited Labeled Data](<https://www.sota2.com/research/paper/68fa67521e209131cea15705>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:11:14.110Z

Authors: Wanyu Lin, Zhaolin Gao, Baochun Li

Published: 2019-10-28T21:23:01.000Z

## Abstract

Graph-based semi-supervised learning has been shown to be one of the most effective approaches for classification tasks from a wide range of domains, such as image classification and text classification, as they can exploit the connectivity patterns between labeled and unlabeled samples to improve learning performance. In this work, we advance this effective learning paradigm towards a scenario where labeled data are severely limited. More specifically, we address the problem of graph-based semi-supervised learning in the presence of severely limited labeled samples, and propose a new framework, called {\\em Shoestring}, that improves the learning performance through semantic transfer from these very few labeled samples to large numbers of unlabeled samples.   In particular, our framework learns a metric space in which classification can be performed by computing the similarity to centroid embedding of each class. {\\em Shoestring} is trained in an end-to-end fashion to learn to leverage the semantic knowledge of limited labeled samples as well as their connectivity patterns with large numbers of unlabeled samples simultaneously. By combining {\\em Shoestring} with graph convolutional networks, label propagation and their recent label-efficient variations (IGCN and GLP), we are able to achieve state-of-the-art node classification performance in the presence of very few labeled samples. In addition, we demonstrate the effectiveness of our framework on image classification tasks in the few-shot learning regime, with significant gains on miniImageNet ($2.57\\%\\sim3.59\\%$) and tieredImageNet ($1.05\\%\\sim2.70\\%$).

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1910.12976>)

[PDF](<https://arxiv.org/pdf/1910.12976.pdf>)