We present an efficient alternative to the convolutional layer using cheap spatial transformations. This construction exploits an inherent spatial redundancy of the learned convolutional filters to enable a much greater parameter efficiency, while maintaining the top-end accuracy of their dense counter-parts. Training these networks is modelled as a generalised pruning problem, whereby the pruned filters are replaced with cheap transformations from the set of non-pruned filters. We provide an efficient implementation of the proposed layer, followed by two natural extensions to avoid excessive feature compression and to improve the expressivity of the transformed features. We show that these networks can achieve comparable or improved performance to state-of-the-art pruning models across both the CIFAR-10 and ImageNet-1K datasets.

翻译：我们提出了一种利用廉价空间变换替代卷积层的高效方案。该构造利用已学习卷积滤波器的固有空间冗余性，在保持与密集网络同等顶尖精度的同时，显著提升参数效率。这些网络的训练过程被建模为广义剪枝问题，其中被剪枝的滤波器由非剪枝滤波器集合的廉价变换替代。我们给出了所提层的高效实现方案，并提出了两种自然扩展以避免过度特征压缩并提升变换特征的表达能力。实验表明，在CIFAR-10和ImageNet-1K数据集上，该网络能够达到或超越当前最优剪枝模型的性能。