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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Balanced Decoupled Spatial Convolution for CNNs
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Balanced Decoupled Spatial Convolution for CNNs

机译：CNN的平衡解耦空间卷积

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摘要

In this paper, we are interested in designing lightweight CNNs by decoupling the convolution along the spatial and channel dimension. Most existing decoupling techniques focus on approximating the filter matrix through decomposition. In contrast, we provide a decoupled view of the standard convolution to separate the spatial information and the channel information. The resulting decoupled process is exactly equivalent to the standard convolution. Inspired from our decoupled view, we propose an effective structure, balanced decoupled spatial convolution (BDSC), to relax the sparsity of the filter in spatial aggregation by learning a spatial configuration and reduce the redundancy by reducing the number of intermediate channels. We also designed an adaptive spatial configuration, which is simply adding a nonlinear activation layer [rectified linear units (ReLU)] after the intermediate output. Our experiments verify that the adaptive spatial configuration can improve the classification performance without extra cost. In addition, our BDSC achieves comparable classification performance with the standard convolution but with a smaller model size on Canadian Institute for Advanced Research (CIFAR)-100, CIFAR-10, and ImageNet. To show the potential of further reducing the redundancy of across channel-domain convolution, we also show experiments of our models with a designed lightweight across channel-domain convolution. Finally, we show in our experiments that our models achieve superior performance than the state-of-the-art models.
机译：在本文中，我们有兴趣通过沿空间和通道维解卷积来设计轻型CNN。大多数现有的去耦技术都集中在通过分解来近似滤波器矩阵上。相反，我们提供了标准卷积的分离视图，以分离空间信息和通道信息。所产生的解耦过程与标准卷积完全等效。从我们的解耦观点出发，我们提出了一种有效的结构，即平衡解耦空间卷积（BDSC），以通过学习空间配置来放松滤波器在空间聚集中的稀疏性，并通过减少中间通道的数量来减少冗余。我们还设计了一种自适应空间配置，该配置只是在中间输出之后简单地添加了一个非线性激活层[整流线性单位（ReLU）]。我们的实验证明，自适应空间配置可以提高分类性能而无需额外成本。此外，我们的BDSC在标准卷积方面达到了可比的分类性能，但在加拿大高级研究所（CIFAR）-100，CIFAR-10和ImageNet上具有较小的模型大小。为了显示进一步减少跨通道域卷积的冗余的潜力，我们还展示了使用设计好的轻量级跨通道域卷积的模型实验。最后，我们在实验中证明我们的模型比最新模型具有更高的性能。

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