TRR-LGF: a Simple yet Efficient Classification Network

Hybrid models that combine convolution and self attention are popular for efficient local feature extraction and capturing long-range dependencies. However, these models often:1) only explore local and global features; 2) flatten high-order features at the output layer, which limit feature hierarchy exploration and the feature utility in the output layer. To address these issues, this paper introduces Tensor Ring Regression with Local-to-Global Features (TRR-LGF), a simple and effective classification network. It uses a local-to-global learning framework to capture diverse features at multiple scales. Additionally, a tensor ring regression layer replaces the linear output layer, preserving high-order feature structure and reducing parameters. Experimental results show that TRR-LGF outperforms existing state-of-the-art methods on various datasets, especially in noisy and sample-imbalanced settings. Furthermore, the model utilizes 7.6M parameters, reducing computational requirements by about 50% compared to the multilayer perceptron output layer. The code is available at https://github.com/Calcium-Oxide/TRR-LGF.