Understanding How Feature Structure Transfers in Transfer Learning
Authors: Tongliang Liu, Qiang Yang, Dacheng Tao
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose a general analysis scheme to theoretically justify that if the source and target domains share similar feature structures, the source domain feature structure is transferable to the target domain, regardless of the change of the labeling functions across domains. The transferred structure is interpreted to function as a regularization matrix which beneļ¬ts the learning process of the target domain task. We prove that such transfer enables the corresponding learning algorithms to be uniformly stable. |
| Researcher Affiliation | Collaboration | Tongliang Liu , Qiang Yang , Dacheng Tao UBTech Sydney AI Institute and SIT, FEIT, The University of Sydney, Australia Hong Kong University of Science and Technology, Hong Kong |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks; it primarily focuses on mathematical formulations and proofs. |
| Open Source Code | No | The paper does not provide any information or links regarding open-source code for the described methodology. The paper is theoretical and does not present an implementation. |
| Open Datasets | No | The paper is purely theoretical and does not use datasets for empirical training or evaluation. It references external empirical studies but does not conduct its own. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments, thus no training/validation/test splits are mentioned or used. |
| Hardware Specification | No | The paper is theoretical and does not involve empirical experiments, so no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe software implementations or experiments, thus no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, thus no experimental setup details like hyperparameters or training configurations are provided. |