Cascade wavelet transform based convolutional neural networks with application to image classification

Pooling has been the core ingredient of modern convolutional neural networks (CNNs). Although classic pooling methods are simple and effective, it will inevitably lead to the problem that some features that make a great contribution to classification may be ignored. To solve this issue, this paper presents a novel cascade wavelet transform module, which makes full use of different frequency components and can be seamlessly integrated into the existing CNNs by replacing the existing pooling operation. In our method, wavelet transforms are performed in both spatial and channel domain. In spatial domain, using 2D discrete wavelet transform, we design a spatial pooling layer with attention mechanism by integrating low-frequency and high-frequency information. In channel domain, based on 1D discrete wavelet transform, a channel pooling layer with the attention mechanism is proposed for the final feature reconstruction. We call the proposed cascade wavelet transform based CNNs CasDWTNets. Compared to the traditional CNNs, experiments demonstrate that CasDWTNets obtain outstanding consistency and accuracy in image classification. Code will be made available.