WAVELET-BASED FREQUENCY-DIVIDING INTERACTIVE CNN FOR IMAGE CLASSIFICATION

The vanilla tensor in convolutional neural networks (CNNs) can be seen as a mixture of feature information at different frequencies, which is currently only used as a carrier of information. However, few people notice that vanilla tensor is spatial information redundant and information interaction of different frequency bands is beneficial for CNNs. In this paper, we design a novel Wavelet-based frequency-dividing interactive block (WFDI) to factorize a vanilla tensor into a pair of tensors with complementary information to reduce redundancy. Based on this, we embed it into the CNN (WFDI-CNN) for image classification. Specifically, the WFDI-CNN factorizes the vanilla tensor into a low-frequency tensor with lower spatial resolution and a high-frequency tensor with complementary information. Then, the information interaction and forward propagation between the high-frequency and low-frequency tensors not only save computational resources but also improve the network performance. Experimental results on CIFAR10 and CIFAR100 datasets all demonstrate the effectiveness of the proposed WFDI block.