We propose a convolutional neural network (CNN) architecture for image classification based on subband decomposition of the image using wavelets. The proposed architecture decomposes the input image spectra into multiple critically sampled subbands, extracts features using a single CNN per subband, and finally, performs classification by combining the extracted features using a fully connected layer. Processing each of the subbands by an individual CNN, thereby limiting the learning scope of each CNN to a single subband, imposes a form of structural regularization. This provides better generalization capability as seen by the presented results. The proposed architecture achieves best-in-class performance in terms of total multiply-add-accumulator operations and nearly best-in-class performance in terms of total parameters required, yet it maintains competitive classification performance. We also show the proposed architecture is more robust than the regular full-band CNN to noise caused by weight-and-bias quantization and input quantization.

翻译：我们提议以子波子图像分解法为基础,为图像分类建立一个革命神经网络结构(CNN)结构。拟议结构将输入图像光谱分解成多个关键抽样子波段,利用单一CNN每个子波段进行提取功能,最后通过使用完全连接的层将提取的特征合并进行分类。由单个CNN处理每个子带,从而将每个CNN的学习范围限制为单一子波段,从而实行某种形式的结构规范化。这提供了更好的概括化能力,从所介绍的结果中可以看出。拟议的结构在总增殖累积器操作和总参数方面达到类内最佳性能,但保持竞争性的分类性能。我们还显示,拟议的结构比普通全波段CNN的强度强,比普通全波段CNN的强度还强,因为重量和偏移和输入四分法造成的噪音。