论文标题
热蛋糕：高级塔克铰接式核，以进行更深的CNN压缩
HOTCAKE: Higher Order Tucker Articulated Kernels for Deeper CNN Compression
论文作者
论文摘要
新兴的边缘计算在不牺牲太多准确性的情况下促进了压实神经网络的巨大兴趣。在这方面，低量张量分解构成了一种强大的工具，可以通过将4向核张量分解为多阶段较小的工具来压缩卷积神经网络（CNN）。在Tucker-2分解之上，我们提出了一个概括的高级塔克铰接式内核（Hotcake）方案，该方案包括四个步骤：输入通道分解，指导的塔克等级选择，高阶Tucker分解和微调。通过将每个Conv层的热蛋糕施加，可以获得具有优美精确度权衡的高度压缩的CNN模型。实验表明，热蛋糕甚至可以压缩预压缩模型并产生最先进的轻量级网络。
The emerging edge computing has promoted immense interests in compacting a neural network without sacrificing much accuracy. In this regard, low-rank tensor decomposition constitutes a powerful tool to compress convolutional neural networks (CNNs) by decomposing the 4-way kernel tensor into multi-stage smaller ones. Building on top of Tucker-2 decomposition, we propose a generalized Higher Order Tucker Articulated Kernels (HOTCAKE) scheme comprising four steps: input channel decomposition, guided Tucker rank selection, higher order Tucker decomposition and fine-tuning. By subjecting each CONV layer to HOTCAKE, a highly compressed CNN model with graceful accuracy trade-off is obtained. Experiments show HOTCAKE can compress even pre-compressed models and produce state-of-the-art lightweight networks.
