用可学习的二维分离变换构建轻量图像识别网络，参数减少90%以上。
Compact and Efficient Neural Networks for Image Recognition Based on Learned 2D Separable Transform
- 用共享权重的两层全连接结构处理图像行与列，实现二维分离计算
- 单层LST+全连接在MNIST上达98.02%准确率，仅9.5k参数
- 已在FPGA实现，适合嵌入式部署的高效紧凑模型设计
本文提出一种可学习的二维分离变换（LST），可作为图像识别神经网络架构的新计算层。该方法通过共享一个全连接层的权重来处理图像所有行，再用第二个共享全连接层处理第一层输出的图像表示的所有列。将LST层用于神经网络架构，相比使用堆叠全连接层的模型，显著减少了参数量。我们展示了一个基于单个LST层加全连接层的分类器，在MNIST数据集上达到98.02%的准确率，仅需9.5k参数。此外，我们在FPGA平台上实现了基于LST的分类器，验证了该方法在构建紧凑且高性能神经网络模型方面的效率。补充材料仓库：https://github.com/Mak-Sim/LST-2d
原文摘要 · Abstract (English)
The paper presents a learned two-dimensional separable transform (LST) that can be considered as a new type of computational layer for constructing neural network (NN) architecture for image recognition tasks. The LST based on the idea of sharing the weights of one fullyconnected (FC) layer to process all rows of an image. After that, a second shared FC layer is used to process all columns of image representation obtained from the first layer. The use of LST layers in a NN architecture significantly reduces the number of model parameters compared to models that use stacked FC layers. We show that a NN-classifier based on a single LST layer followed by an FC layer achieves 98.02\% accuracy on the MNIST dataset, while having only 9.5k parameters. We also implemented a LST-based classifier for handwritten digit recognition on the FPGA platform to demonstrate the efficiency of the suggested approach for designing a compact and high-performance implementation of NN models. Git repository with supplementary materials: https://github.com/Mak-Sim/LST-2d
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