🤖 AI Summary
Conventional transfer learning assumes structural homogeneity between source and target models (e.g., regression → regression), limiting applicability to cross-task settings (e.g., regression → classification) and small-sample regimes where both source and target data are scarce.
Method: We propose a decoupled transfer learning framework that explicitly separates representation sufficiency from task-structure dependency. It jointly estimates sufficient statistics and learns invariant representations from the source domain, then augments them with target-domain independent components—enabling theoretically grounded representation enhancement without requiring model homomorphism.
Contribution/Results: The framework guarantees theoretical sufficiency and consistency of learned representations. It supports heterogeneous task transfer and few-shot knowledge adaptation. Empirical evaluation on synthetic and real-world benchmarks demonstrates significant improvements in generalization performance, particularly under extreme target-data scarcity—outperforming state-of-the-art homomorphic and adversarial transfer methods.
📝 Abstract
Transfer learning is an important approach for addressing the challenges posed by limited data availability in various applications. It accomplishes this by transferring knowledge from well-established source domains to a less familiar target domain. However, traditional transfer learning methods often face difficulties due to rigid model assumptions and the need for a high degree of similarity between source and target domain models. In this paper, we introduce a novel method for transfer learning called Transfer learning through Enhanced Sufficient Representation (TESR). Our approach begins by estimating a sufficient and invariant representation from the source domains. This representation is then enhanced with an independent component derived from the target data, ensuring that it is sufficient for the target domain and adaptable to its specific characteristics. A notable advantage of TESR is that it does not rely on assuming similar model structures across different tasks. For example, the source domain models can be regression models, while the target domain task can be classification. This flexibility makes TESR applicable to a wide range of supervised learning problems. We explore the theoretical properties of TESR and validate its performance through simulation studies and real-world data applications, demonstrating its effectiveness in finite sample settings.