Filter-in-Filter: Low Cost CNN Improvement by Sub-filter Parameter Sharing.

•We defined the sub-filters of a filter and visualized them to verify these sub-filters can recognize multiple meaningful patterns.•Filter-in-Filter was proposed to make full use of the sub-filters to enhance the expressibility of the filters in CNNs.•Filter-in-Filter does not increase the number of parameters and increases the computational cost only slightly as compared to the standard convolution. We verified that FIF is effective to improve the performance of CNNs by conducting extensive experiments.