Interpretable Few-Shot Learning via Linear Distillation

Arip Asadulaev; Igor Kuznetsov; Andrey Filchenkov

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arXiv · 1906.05431

Interpretable Few-Shot Learning via Linear Distillation

Arip Asadulaev·Igor Kuznetsov·Andrey Filchenkov

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Abstract

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.

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BibTeX RIS

Arip Asadulaev, Igor Kuznetsov, Andrey Filchenkov. 2019-06-13. Interpretable Few-Shot Learning via Linear Distillation. https://arxiv.org/abs/1906.05431

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