  The channel redundancy in feature maps of convolutional neural networks
(CNNs) results in the large consumption of memories and computational
resources. In this work, we design a novel Slim Convolution (SlimConv) module
to boost the performance of CNNs by reducing channel redundancies. Our SlimConv
consists of three main steps: Reconstruct, Transform and Fuse, through which
the features are splitted and reorganized in a more efficient way, such that
the learned weights can be compressed effectively. In particular, the core of
our model is a weight flipping operation which can largely improve the feature
diversities, contributing to the performance crucially. Our SlimConv is a
plug-and-play architectural unit which can be used to replace convolutional
layers in CNNs directly. We validate the effectiveness of SlimConv by
conducting comprehensive experiments on ImageNet, MS COCO2014, Pascal VOC2012
segmentation, and Pascal VOC2007 detection datasets. The experiments show that
SlimConv-equipped models can achieve better performances consistently, less
consumption of memory and computation resources than non-equipped conterparts.
For example, the ResNet-101 fitted with SlimConv achieves 77.84% top-1
classification accuracy with 4.87 GFLOPs and 27.96M parameters on ImageNet,
which shows almost 0.5% better performance with about 3 GFLOPs and 38%
parameters reduced.
