# Unsupervised Data Selection for Supervised Learning

arXiv: https://arxiv.org/abs/1810.12142 · HTML: https://mlsanity.com/paper/1810.12142/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2018.

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

- [Extending Contrastive Learning to Unsupervised Coreset Selection](https://mlsanity.com/paper/2103.03574/) (2021)

## Abstract (arXiv)

Recent research put a big effort in the development of deep learning architectures and optimizers obtaining impressive results in areas ranging from vision to language processing. However little attention has been addressed to the need of a methodological process of data collection. In this work we hypothesize that high quality data for supervised learning can be selected in an unsupervised manner and that by doing so one can obtain models capable to generalize better than in the case of random training set construction. However, preliminary results are not robust and further studies on the subject should be carried out.