# Metric learning by Similarity Network for Deep Semi-Supervised Learning

arXiv: https://arxiv.org/abs/2004.14227 · HTML: https://mlsanity.com/paper/2004.14227/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2020.

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

- [Applying SoftTriple Loss for Supervised Language Model Fine Tuning](https://mlsanity.com/paper/2112.08462/) (2021)

## Abstract (arXiv)

Deep semi-supervised learning has been widely implemented in the real-world due to the rapid development of deep learning. Recently, attention has shifted to the approaches such as Mean-Teacher to penalize the inconsistency between two perturbed input sets. Although these methods may achieve positive results, they ignore the relationship information between data instances. To solve this problem, we propose a novel method named Metric Learning by Similarity Network (MLSN), which aims to learn a distance metric adaptively on different domains. By co-training with the classification network, similarity network can learn more information about pairwise relationships and performs better on some empirical tasks than state-of-art methods.