Frequency-Aware Residual Networks with Masked Dual-Path Convolution for Image Classification

Recent deep learning-based models have demonstrated outstanding performance in various computer vision tasks, including image classification. Among them, ResNet is a representative model that achieves high accuracy by utilizing a deep network architecture. However, as the depth of ResNet increases, the accumulated computational cost leads to higher overall FLOPs, which in turn results in increased computational burden and processing time. To address this issue, this paper proposes a novel model architecture that improves computational efficiency while maintaining accuracy. The proposed model reduces the total computational cost by separating the input image into high-frequency and low-frequency regions and applying convolution operations specialized to each region. Experimental results show that the proposed method reduces FLOPs by up to 29% compared to the original ResNet while maintaining competitive classification performance. In addition, by incorporating depthwise convolution instead of standard convolution, the model achieves an average reduction of 14.3% in operations compared to the standard multi-path model while reducing top-1 accuracy by only 0.18%.