# Out-distribution aware Self-training in an Open World Setting

arXiv: https://arxiv.org/abs/2012.12372 · HTML: https://mlsanity.com/paper/2012.12372/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2020.

## Built on by (most cited first, 6 of 6)

- [Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization](https://mlsanity.com/paper/2107.04649/) (2021)
- [Semi-Supervised and Unsupervised Deep Visual Learning: A Survey](https://mlsanity.com/paper/2208.11296/) (2022)
- [Combining Diverse Feature Priors](https://mlsanity.com/paper/2110.08220/) (2021)
- [RoPAWS: Robust Semi-supervised Representation Learning from Uncurated Data](https://mlsanity.com/paper/2302.14483/) (2023)
- [Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning](https://mlsanity.com/paper/2305.18158/) (2023)
- [Out-of-distribution data supervision towards biomedical semantic segmentation](https://mlsanity.com/paper/2507.12105/) (2025)

## Abstract (arXiv)

Deep Learning heavily depends on large labeled datasets which limits further improvements. While unlabeled data is available in large amounts, in particular in image recognition, it does not fulfill the closed world assumption of semi-supervised learning that all unlabeled data are task-related. The goal of this paper is to leverage unlabeled data in an open world setting to further improve prediction performance. For this purpose, we introduce out-distribution aware self-training, which includes a careful sample selection strategy based on the confidence of the classifier. While normal self-training deteriorates prediction performance, our iterative scheme improves using up to 15 times the amount of originally labeled data. Moreover, our classifiers are by design out-distribution aware and can thus distinguish task-related inputs from unrelated ones.