Shoestring: Graph-Based Semi-Supervised Classification With Severely Limited Labeled Data

no code implementations • CVPR 2020 • Wan-Yu Lin, Zhaolin Gao, Baochun Li

To address the problem of semi-supervised learning in the presence of severely limited labeled samples, we propose a new framework, called Shoestring , that incorporates metric learning into the paradigm of graph-based semi-supervised learning.

Classification Clustering +8

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

1 code implementation • 28 Oct 2019 • Wan-Yu Lin, Zhaolin Gao, Baochun Li

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.

Few-Shot Learning General Classification +4

Online Plagiarized Detection Through Exploiting Lexical, Syntax, and Semantic Information

no code implementations • ACL 2012 • Wan-Yu Lin, Nanyun Peng, Chun-Chao Yen, Shou-De Lin

Semantic Textual Similarity