# IGCV2: Interleaved Structured Sparse Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1804.06202 · HTML: https://mlsanity.com/paper/1804.06202/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2018.

## Built on by (most cited first, 12 of 12)

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- [FalconNet: Factorization for the Light-weight ConvNets](https://mlsanity.com/paper/2306.06365/) (2023)
- [Whole Brain Segmentation with Full Volume Neural Network](https://mlsanity.com/paper/2110.15601/) (2021)
- [Progressive Channel-Shrinking Network](https://mlsanity.com/paper/2304.00280/) (2023)
- [Learnable Heterogeneous Convolution: Learning both topology and strength](https://mlsanity.com/paper/2301.05440/) (2023)
- [Boosting Convolutional Neural Networks with Middle Spectrum Grouped Convolution](https://mlsanity.com/paper/2304.06305/) (2023)
- [Parallel mesh reconstruction streams for pose estimation of interacting hands](https://mlsanity.com/paper/2104.12123/) (2021)
- [On The Energy Statistics of Feature Maps in Pruning of Neural Networks with Skip-Connections](https://mlsanity.com/paper/2201.11209/) (2022)
- [Expert-Like Reparameterization of Heterogeneous Pyramid Receptive Fields in Efficient CNNs for Fair Medical Image Classification](https://mlsanity.com/paper/2505.13039/) (2025)

## Abstract (arXiv)

In this paper, we study the problem of designing efficient convolutional neural network architectures with the interest in eliminating the redundancy in convolution kernels. In addition to structured sparse kernels, low-rank kernels and the product of low-rank kernels, the product of structured sparse kernels, which is a framework for interpreting the recently-developed interleaved group convolutions (IGC) and its variants (e.g., Xception), has been attracting increasing interests. Motivated by the observation that the convolutions contained in a group convolution in IGC can be further decomposed in the same manner, we present a modularized building block, IGCV2: interleaved structured sparse convolutions. It generalizes interleaved group convolutions, which is composed of two structured sparse kernels, to the product of more structured sparse kernels, further eliminating the redundancy. We present the complementary condition and the balance condition to guide the design of structured sparse kernels, obtaining a balance among three aspects: model size, computation complexity and classification accuracy. Experimental results demonstrate the advantage on the balance among these three aspects compared to interleaved group convolutions and Xception, and competitive performance compared to other state-of-the-art architecture design methods.