  Filter pruning has been widely used for compressing convolutional neural
networks to reduce computation costs during the deployment stage. Recent
studies have shown that filter pruning techniques can achieve lossless
compression of deep neural networks, reducing redundant filters (kernels)
without sacrificing accuracy performance. However, the evaluation is done when
the training and testing data are from similar environmental conditions
(independent and identically distributed), and how the filter pruning
techniques would affect the cross-domain generalization (out-of-distribution)
performance is largely ignored. We conduct extensive empirical experiments and
reveal that although the intra-domain performance could be maintained after
filter pruning, the cross-domain performance will decay to a large extent. As
scoring a filter's importance is one of the central problems for pruning, we
design the importance scoring estimation by using the variance of domain-level
risks to consider the pruning risk in the unseen distribution. As such, we can
remain more domain generalized filters. The experiments show that under the
same pruning ratio, our method can achieve significantly better cross-domain
generalization performance than the baseline filter pruning method. For the
first attempt, our work sheds light on the joint problem of domain
generalization and filter pruning research.
