  As deep neural networks (DNNs) are increasingly used in safety-critical
applications, there is a growing concern for their reliability. Even highly
trained, high-performant networks are not 100% accurate. However, it is very
difficult to predict their behavior during deployment without ground truth. In
this paper, we provide a comparative and replicability study on recent
approaches that have been proposed to evaluate the reliability of DNNs in
deployment. We find that it is hard to run and reproduce the results for these
approaches on their replication packages and even more difficult to run them on
artifacts other than their own. Further, it is difficult to compare the
effectiveness of the approaches, due to the lack of clearly defined evaluation
metrics. Our results indicate that more effort is needed in our research
community to obtain sound techniques for evaluating the reliability of neural
networks in safety-critical domains. To this end, we contribute an evaluation
framework that incorporates the considered approaches and enables evaluation on
common benchmarks, using common metrics.
