论文标题
瞬时使用卷积神经网络分类的重要性
The Importance of the Instantaneous Phase for classification using Convolutional Neural Networks
论文作者
论文摘要
在计算资源方面，卷积神经网络（CNN）的大规模培训非常要求。同样，对于特定应用，转移学习的标准使用还倾向于需要更多的资源。这项工作研究了使用AM-FM表示作为CNN分类应用程序的输入图像的影响。对AM-FM组件组合和灰度图像进行了比较，作为减少和完整网络的输入。结果表明，仅相分量在简单网络中产生了重要的预测。 IA或灰度图像都无法诱导系统中的任何学习。此外，在训练过程中，FM结果比最先进的MobilenEtV2体系结构的参数更快，使用123倍的参数，同时保持可比性的性能（AUC为0.78 vs 0.79）。
Large-scale training of Convolutional Neural Networks (CNN) is extremely demanding in terms of computational resources. Also, for specific applications, the standard use of transfer learning also tends to require far more resources than what may be needed. This work examines the impact of using AM-FM representations as input images for CNN classification applications. A comparison was made between AM-FM components combinations and grayscale images as inputs for reduced and complete networks. The results showed that only the phase component produced significant predictions within a simple network. Neither IA or gray scale image were able to induce any learning in the system. Furthermore, the FM results were 7x faster during training and used 123x less parameters compared to state-of-the-art MobileNetV2 architecture, while maintaining comparable performance (AUC of 0.78 vs 0.79).
