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In this paper, we are interested in building lightweight and efficientconvolutional neural networks. Inspired by the success of two design patterns,composition of structured sparse kernels, e.g., interleaved group convolutions(IGC), and composition of low-rank kernels, e.g., bottle-neck modules, we studythe combination of such two design patterns, using the composition ofstructured sparse low-rank kernels, to form a convolutional kernel. Rather thanintroducing a complementary condition over channels, we introduce a loosecomplementary condition, which is formulated by imposing the complementarycondition over super-channels, to guide the design for generating a denseconvolutional kernel. The resulting network is called IGCV3. We empiricallydemonstrate that the combination of low-rank and sparse kernels boosts theperformance and the superiority of our proposed approach to thestate-of-the-arts, IGCV2 and MobileNetV2 over image classification on CIFAR andImageNet and object detection on COCO.

IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks