Few-Shot and Domain Adaptation Modeling for Evaluating Growth Strategies in Long-Tail Small and Medium-sized Enterprises

Wenwen Liu

doi:10.70393/6a69656173.333434

Authors

Wenwen Liu University of Washington

DOI:

https://doi.org/10.70393/6a69656173.333434

ARK:

https://n2t.net/ark:/40704/JIEAS.v3n6a05

Disciplines:

Artificial Intelligence Technology

Subjects:

Machine Learning

References:

5

Keywords:

SMEs, Growth Strategies, Domain Adaptation, Few-shot Learning, Strategy Optimization

Abstract

To enhance the execution of growth strategies for SMEs under data sparsity and domain shift, this study combines domain adaptation with few-shot learning to identify growth bottlenecks and generate actionable strategies through model optimization. Practical case studies validate the model's applicability and adaptability across domains. By integrating feature alignment and reweighting mechanisms, the strategy significantly improves performance in long-tail categories and cross-domain transferability.

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Author Biography

Wenwen Liu, University of Washington

University of Washington, USA.

References

[1] Zhang, Y., Zhang, M., & Liu, W. (2025). Joint distribution domain adaptation: A novel meta-learning framework for cross-domain few-shot fault diagnosis. Journal of Intelligent Manufacturing, Advance online publication, 1–22. https://doi.org/10.1007/s10845-025-02708-z

[2] Zou, Y., Gu, C., Yu, J., et al. (2025). Incremental pseudo-labeling for black-box unsupervised domain adaptation. Journal of Visual Communication and Image Representation, 113, 104630. https://doi.org/10.1016/j.jvcir.2025.104630

[3] Chen, Y., Zhu, X., Li, Y., et al. (2026). Contrast and clustering: Learning neighborhood pair representation for source-free domain adaptation. Signal Processing: Image Communication, 140, 117429. https://doi.org/10.1016/j.image.2025.117429

[4] Wang, S., Fu, Y., & Kim, J. (2026). Toward construction-specialized, small language models: The interplay of domain adaptation, model scale and data volume. Advanced Engineering Informatics, 69, 104035. https://doi.org/10.1016/j.aei.2025.104035

[5] Zhang, W., Ye, P., Chen, D., et al. (2026). ADA framework for unsupervised domain adaptation person re-identification. Pattern Recognition, 171, 112238. https://doi.org/10.1016/j.patcog.2025.112238