SmRNet: Scalable Multiresolution Feature Extraction Network

This work addresses the challenge of designing a lightweight convolutional neural network (CNN) with high accuracy. To overcome information loss due to pooling operation, the proposed approach leverages the discrete wavelet transform (DWT) and inverse wavelet transform (IWT) as downsampling and upsampling operators instead of pooling and stride operations. The lost details during downsampling can be effectively restored by exploiting the lossless property of wavelets. As opposed to using a single filter, as in traditional convolution, this method employs both low- and high-frequency filters. The low-frequency filter captures information similar to the spatial domain representation, while the high-frequency filter extracts additional feature information. By incorporating both components, we enhance the classification and detection performance, as the combined information provides a richer representation of the input data. The depth-wise separable convolution is also employed to optimize the model further by reducing the number of parameters. Additionally, the proposed network offers flexibility with three options: small, medium, and large, allowing users to choose the appropriate size based on their specific requirements. Furthermore, the network can be easily expanded by adding more layers for those who desire further customization. The experimental evaluations on the CIFAR-10 and CIFAR-100 datasets for classification and MS COCO and Pascal VOC datasets for object detection demonstrate that the proposed feature extraction network outperforms ResNet-50, VGG16, and other networks in terms of accuracy while using fewer parameters. Notably, our approach shows significant promise for small object classification. The proposed network can be a general backbone for image classification, object detection, segmentation, and related tasks. The code has been released.