  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.
