Home Page

Papers

Submissions

News

Editorial Board

Proceedings

Open Source Software

Search

Statistics

Login

Frequently Asked Questions

Contact Us



RSS Feed

ProtoAttend: Attention-Based Prototypical Learning

Sercan O. Arik, Tomas Pfister; 21(210):1−35, 2020.

Abstract

We propose a novel inherently interpretable machine learning method that bases decisions on few relevant examples that we call prototypes. Our method, ProtoAttend, can be integrated into a wide range of neural network architectures including pre-trained models. It utilizes an attention mechanism that relates the encoded representations to samples in order to determine prototypes. Protoattend yields superior results in three high impact problems without sacrificing accuracy of the original model: (1)it enables high-quality interpretability that outputs samples most relevant to the decision-making (i.e. a sample-based interpretability method); (2) it achieves state of the art confidence estimation by quantifying the mismatch across prototype labels; and (3) it obtains state of the art in distribution mismatch detection. All these can be achieved with minimal additional test time and a practically viable training time computational cost.

[abs][pdf][bib]       
© JMLR 2020. (edit, beta)