A Multi-layer Convolutional Sparse Coding Network Based on Multi-Scale

In recent years, the Multi-layer convolutional sparse coding (ML-CSC) model has been regarded as a theoretical explanation for convolutional neural networks (CNN). While the ML-CSC model performs well on datasets with high feature contrast, its performance is not satisfactory on datasets with low feature contrast. To address this issue, this paper introduces a multi-scale technique to design a multi-scale multi-layer convolutional sparse coding network (MSMCSCNet), which not only achieves better image classification results in scenarios with weak feature contrast, but also provides the model with a solid theoretical foundation and higher interpretability. Experimental results demonstrate that, without increasing the parameter count, MSMCSCNet achieves accuracy improvements of 5.75, 9.75, and 9.8 percentage points on the Cifar10, Cifar100 datasets, and the Imagenet32 subset, respectively, compared to existing ML-CSC models. Furthermore, ablation experiments further validate the effectiveness of the model's multi-scale design and feature selection mechanism.