AIREPAIR: A Repair Platform for Neural Networks

TL;DR

AIREPAIR is a platform that facilitates the comparison and evaluation of various neural network repair methods on the same models, aiding in the development of more effective repair techniques.

Contribution

It introduces a unified platform integrating multiple repair tools, enabling fair comparison and analysis of neural network repair methods.

Findings

AIREPAIR effectively compares different repair techniques.

Evaluation on popular datasets demonstrates the platform's utility.

Analysis highlights strengths and weaknesses of existing repair methods.

Abstract

We present AIREPAIR, a platform for repairing neural networks. It features the integration of existing network repair tools. Based on AIREPAIR, one can run different repair methods on the same model, thus enabling the fair comparison of different repair techniques. We evaluate AIREPAIR with three state-of-the-art repair tools on popular deep-learning datasets and models. Our evaluation confirms the utility of AIREPAIR, by comparing and analyzing the results from different repair techniques. A demonstration is available at https://youtu.be/UkKw5neeWhw.