  Network pruning is a method for reducing test-time computational resource
requirements with minimal performance degradation. Conventional wisdom of
pruning algorithms suggests that: (1) Pruning methods exploit information from
training data to find good subnetworks; (2) The architecture of the pruned
network is crucial for good performance. In this paper, we conduct sanity
checks for the above beliefs on several recent unstructured pruning methods and
surprisingly find that: (1) A set of methods which aims to find good
subnetworks of the randomly-initialized network (which we call "initial
tickets"), hardly exploits any information from the training data; (2) For the
pruned networks obtained by these methods, randomly changing the preserved
weights in each layer, while keeping the total number of preserved weights
unchanged per layer, does not affect the final performance. These findings
inspire us to choose a series of simple \emph{data-independent} prune ratios
for each layer, and randomly prune each layer accordingly to get a subnetwork
(which we call "random tickets"). Experimental results show that our zero-shot
random tickets outperform or attain a similar performance compared to existing
"initial tickets". In addition, we identify one existing pruning method that
passes our sanity checks. We hybridize the ratios in our random ticket with
this method and propose a new method called "hybrid tickets", which achieves
further improvement. (Our code is publicly available at
https://github.com/JingtongSu/sanity-checking-pruning)
