# Interpretable Few-Shot Learning via Linear Distillation

arXiv: https://arxiv.org/abs/1906.05431 · HTML: https://mlsanity.com/paper/1906.05431/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2019.

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

- [Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning](https://mlsanity.com/paper/2203.16464/) (2022)

## Abstract (arXiv)

It is important to develop mathematically tractable models than can interpret knowledge extracted from the data and provide reasonable predictions. In this paper, we present a Linear Distillation Learning, a simple remedy to improve the performance of linear neural networks. Our approach is based on using a linear function for each class in a dataset, which is trained to simulate the output of a teacher linear network for each class separately. We tested our model on MNIST and Omniglot datasets in the Few-Shot learning manner. It showed better results than other interpretable models such as classical Logistic Regression.