终极张量化：压缩卷积层和FC层
Ultimate tensorization: compressing convolutional and FC layers alike
摘要：
卷积神经网络在图像识别任务中表现出色，但这是以高计算和存储复杂性为代价的。为了解决这个问题，[1]开发了一个张量分解框架来压缩全连接层。在本文中，我们专注于压缩卷积层。我们表明，虽然直接将张量框架[1]应用于卷积的4维核确实压缩了层，但我们可以做得更好。我们将卷积核重塑为高阶张量并对其进行因子分解。我们将所提出的方法与之前的工作相结合，以压缩网络的卷积层和全连接层，并在CIFAR-10数据集上实现80倍的网络压缩率，准确率下降1.1%。
英文摘要：
Convolutional neural networks excel in image recognition tasks, but this comes at the cost of high computational and memory complexity. To tackle this problem, [1] developed a tensor factorization framework to compress fully-connected layers. In this paper, we focus on compressing convolutional layers. We show that while the direct application of the tensor framework [1] to the 4-dimensional kernel of convolution does compress the layer, we can do better. We reshape the convolutional kernel into a tensor of higher order and factorize it. We combine the proposed approach with the previous work to compress both convolutional and fully-connected layers of a network and achieve 80x network compression rate with 1.1% accuracy drop on the CIFAR-10 dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
