  Many effective solutions have been proposed to reduce the redundancy of
models for inference acceleration. Nevertheless, common approaches mostly focus
on eliminating less important filters or constructing efficient operations,
while ignoring the pattern redundancy in feature maps. We reveal that many
feature maps within a layer share similar but not identical patterns. However,
it is difficult to identify if features with similar patterns are redundant or
contain essential details. Therefore, instead of directly removing uncertain
redundant features, we propose a \textbf{sp}lit based \textbf{conv}olutional
operation, namely SPConv, to tolerate features with similar patterns but
require less computation. Specifically, we split input feature maps into the
representative part and the uncertain redundant part, where intrinsic
information is extracted from the representative part through relatively heavy
computation while tiny hidden details in the uncertain redundant part are
processed with some light-weight operation. To recalibrate and fuse these two
groups of processed features, we propose a parameters-free feature fusion
module. Moreover, our SPConv is formulated to replace the vanilla convolution
in a plug-and-play way. Without any bells and whistles, experimental results on
benchmarks demonstrate SPConv-equipped networks consistently outperform
state-of-the-art baselines in both accuracy and inference time on GPU, with
FLOPs and parameters dropped sharply.
