Transfer Learning through Enhanced Sufficient Representation: Enriching Source Domain Knowledge with Target Data
This work addresses the challenge of limited data availability for practitioners in various supervised learning domains, though it appears incremental as it builds on existing representation-based transfer learning approaches.
The paper tackles the problem of rigid model assumptions and domain similarity requirements in transfer learning by introducing TESR, a method that enhances source domain representations with target data to achieve flexibility across different task types, such as regression to classification, and demonstrates its effectiveness in simulations and real-world applications.
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.