优化Xception模型，让其在边缘设备上更轻量高效。
Depthwise Separable Convolutions with Deep Residual Convolutions
- 用深度可分离卷积结合深层残差结构压缩模型
- 参数量减少，训练时间缩短，检测性能超越原版Xception
- 适合资源受限的边缘设备部署，如移动摄像头
边缘计算的发展促使研究者优化深度学习模型以适应边缘设备。本文针对计算机视觉中广泛应用的Xception架构进行优化，该架构虽在目标检测任务中表现优异，但计算成本高，难以在资源受限的设备上部署。为此，我们提出一种专为边缘设备设计的轻量化改进版Xception，融合深度可分离卷积与深层残差结构，在保持性能的同时显著降低参数量、内存占用和计算负载。实验在CIFAR-10目标检测数据集上验证，结果表明新模型参数更少、训练时间更短，且检测性能优于原始Xception。
原文摘要 · Abstract (English)
The recent advancement of edge computing enables researchers to optimize various deep learning architectures to employ them in edge devices. In this study, we aim to optimize Xception architecture which is one of the most popular deep learning algorithms for computer vision applications. The Xception architecture is highly effective for object detection tasks. However, it comes with a significant computational cost. The computational complexity of Xception sometimes hinders its deployment on resource-constrained edge devices. To address this, we propose an optimized Xception architecture tailored for edge devices, aiming for lightweight and efficient deployment. We incorporate the depthwise separable convolutions with deep residual convolutions of the Xception architecture to develop a small and efficient model for edge devices. The resultant architecture reduces parameters, memory usage, and computational load. The proposed architecture is evaluated on the CIFAR 10 object detection dataset. The evaluation result of our experiment also shows the proposed architecture is smaller in parameter size and requires less training time while outperforming Xception architecture performance.
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