  Neural network pruning is a popular technique used to reduce the inference
costs of modern, potentially overparameterized, networks. Starting from a
pre-trained network, the process is as follows: remove redundant parameters,
retrain, and repeat while maintaining the same test accuracy. The result is a
model that is a fraction of the size of the original with comparable predictive
performance (test accuracy). Here, we reassess and evaluate whether the use of
test accuracy alone in the terminating condition is sufficient to ensure that
the resulting model performs well across a wide spectrum of "harder" metrics
such as generalization to out-of-distribution data and resilience to noise.
Across evaluations on varying architectures and data sets, we find that pruned
networks effectively approximate the unpruned model, however, the prune ratio
at which pruned networks achieve commensurate performance varies significantly
across tasks. These results call into question the extent of \emph{genuine}
overparameterization in deep learning and raise concerns about the
practicability of deploying pruned networks, specifically in the context of
safety-critical systems, unless they are widely evaluated beyond test accuracy
to reliably predict their performance. Our code is available at
https://github.com/lucaslie/torchprune.
