多源迁移学习新框架，用统计不变性融合多样知识，收敛更快更稳定。
SETrLUSI: Stochastic Ensemble Multi-Source Transfer Learning Using Statistical Invariant
- 通过统计不变性整合多源知识，弱化收敛模式提升泛化能力。
- 实验显示收敛速度更快，训练耗时更低，性能优于现有方法。
- 适合需要高效稳定迁移的场景，如跨域分类与小样本学习。
在迁移学习中，源域通常包含多样化知识，不同域侧重不同类型的知识。传统方法仅从所有域中提取单一类型知识，而本文提出一种基于统计不变性（SI）的随机集成多源迁移学习框架（SETrLUSI），可同时提取并融合源域与目标域中的多种知识。该方法不仅有效利用知识多样性，还加速了模型收敛过程。SETrLUSI进一步引入随机SI选择、按比例采样源域及目标域自举机制，提升了训练效率与模型稳定性。实验表明，该方法具备良好收敛性，性能优于相关方法，且时间成本更低。
原文摘要 · Abstract (English)
In transfer learning, a source domain often carries diverse knowledge, and different domains usually emphasize different types of knowledge. Different from handling only a single type of knowledge from all domains in traditional transfer learning methods, we introduce an ensemble learning framework with a weak mode of convergence in the form of Statistical Invariant (SI) for multi-source transfer learning, formulated as Stochastic Ensemble Multi-Source Transfer Learning Using Statistical Invariant (SETrLUSI). The proposed SI extracts and integrates various types of knowledge from both source and target domains, which not only effectively utilizes diverse knowledge but also accelerates the convergence process. Further, SETrLUSI incorporates stochastic SI selection, proportional source domain sampling, and target domain bootstrapping, which improves training efficiency while enhancing model stability. Experiments show that SETrLUSI has good convergence and outperforms related methods with a lower time cost.
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