# Learning from a Teacher using Unlabeled Data

arXiv: https://arxiv.org/abs/1911.05275 · HTML: https://mlsanity.com/paper/1911.05275/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2019.

## Built on by (most cited first, 4 of 4)

- [Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better](https://mlsanity.com/paper/2106.08962/) (2021)
- [Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data](https://mlsanity.com/paper/2110.15094/) (2021)
- [Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions](https://mlsanity.com/paper/2406.10861/) (2024)
- [Knowledge Distillation from Multiple Foundation Models for End-to-End Speech Recognition](https://mlsanity.com/paper/2303.10917/) (2023)

## Abstract (arXiv)

Knowledge distillation is a widely used technique for model compression. We posit that the teacher model used in a distillation setup, captures relationships between classes, that extend beyond the original dataset. We empirically show that a teacher model can transfer this knowledge to a student model even on an it out-of-distribution dataset. Using this approach, we show promising results on MNIST, CIFAR-10, and Caltech-256 datasets using unlabeled image data from different sources. Our results are encouraging and help shed further light from the perspective of understanding knowledge distillation and utilizing unlabeled data to improve model quality.