# Teacher-Student Learning Paradigm for Tri-training: An Efficient Method for Unlabeled Data Exploitation

arXiv: https://arxiv.org/abs/1909.11233 · HTML: https://mlsanity.com/paper/1909.11233/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [Wafer Map Defect Patterns Semi-Supervised Classification Using Latent Vector Representation](https://mlsanity.com/paper/2311.12840/) (2023)

## Abstract (arXiv)

Given that labeled data is expensive to obtain in real-world scenarios, many semi-supervised algorithms have explored the task of exploitation of unlabeled data. Traditional tri-training algorithm and tri-training with disagreement have shown promise in tasks where labeled data is limited. In this work, we introduce a new paradigm for tri-training, mimicking the real world teacher-student learning process. We show that the adaptive teacher-student thresholds used in the proposed method provide more control over the learning process with higher label quality. We perform evaluation on SemEval sentiment analysis task and provide comprehensive comparisons over experimental settings containing varied labeled versus unlabeled data rates. Experimental results show that our method outperforms other strong semi-supervised baselines, while requiring less number of labeled training samples.