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Coarse-to-fine knowledge transfer based long-tailed classification via bilateral-sampling network

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Abstract

Long-tailed classification faces a considerable challenge from the imbalanced distribution of head and tail data. Re-sampling is a traditional Single Branch Sampling (SBS) method used to adjust data imbalances that effectively improves the performance of long-tailed classification models. Most existing SBS models assume that the classes are independent of each other and ignore the hierarchical relations among the classes. However, the hierarchical structure is exhibited as coarse- and fine-grained semantic relations, a significant knowledge to guide long-tailed classification. In this paper, we propose a Coarse-to-Fine knowledge transfer based Bilateral-Sampling Network (CFBSNet) for long-tailed classification that alleviates the effects of imbalances in long-tailed data and considers coarse- and fine-grained semantic relationships. First, we present a Bilateral-Branch Sampling Network consisting of two sampling branches. The two sampling branches perform reverse sampling and uniform sampling, respectively. Second, we design a Coarse-to-Fine Knowledge Transfer strategy that regulates different learning stages by adjusting loss weight in each task progressively. CFBSNet pays attention to the semantic relationship between tail data and granularity. The experimental results demonstrated the effectiveness of CFBSNet for long-tailed classification tasks. For instance, the classification accuracy of CFBSNet is 3.16\(\%\) and 2.62\(\%\) better than that of baseline models on the CIFAR-100-LT and the SUN datasets, respectively.

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Data availability

Some or all data, models, or code generated or used during the study are available from the Data availability corresponding author by request (Hong Zhao).

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Acknowledgements

This work was supported by the Natural Science Foundation of Fujian Province under Grant no. 2021J011003 and the National Natural Science Foundation of China under Grant no. 62141602.

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Authors and Affiliations

  1. School of Computer Science, Minnan Normal University, Zhangzhou, 363000, Fujian, China

    Junyan Xu, Wei Zhao & Hong Zhao

  2. Key Laboratory of Data Science and Intelligence Application, Fujian Province University, Zhangzhou, 363000, Fujian, China

    Junyan Xu, Wei Zhao & Hong Zhao

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  1. Junyan Xu
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  2. Wei Zhao
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  3. Hong Zhao
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Correspondence to Hong Zhao.

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Xu, J., Zhao, W. & Zhao, H. Coarse-to-fine knowledge transfer based long-tailed classification via bilateral-sampling network. Int. J. Mach. Learn. & Cyber. 14, 3323–3336 (2023). https://doi.org/10.1007/s13042-023-01835-4

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