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Knowledge-inspired Subdomain Adaptation for Cross-Domain Knowledge Transfer

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Leye WangAuthors Info & Claims

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Pages 234 - 244

https://doi.org/10.1145/3583780.3614946

Published: 21 October 2023 Publication History

Abstract

Most state-of-the-art deep domain adaptation techniques align source and target samples in a global fashion. That is, after alignment, each source sample is expected to become similar to any target sample. However, global alignment may not always be optimal or necessary in practice. For example, consider cross-domain fraud detection, where there are two types of transactions: credit and non-credit. Aligning credit and non-credit transactions separately may yield better performance than global alignment, as credit transactions are unlikely to exhibit patterns similar to non-credit transactions. To enable such fine-grained domain adaption, we propose a novel Knowledge-Inspired Subdomain Adaptation (KISA) framework. In particular, (1) We provide the theoretical insight that KISA minimizes the shared expected loss which is the premise for the success of domain adaptation methods. (2) We propose the knowledge-inspired subdomain division problem that plays a crucial role in fine-grained domain adaption. (3) We design a knowledge fusion network to exploit diverse domain knowledge. Extensive experiments demonstrate that KISA achieves remarkable results on fraud detection and traffic demand prediction tasks.

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Cited By

Hou CZhou YCao YLiu THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)ECAT: A Entire space Continual and Adaptive Transfer Learning Framework for Cross-Domain RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661348(2885-2889)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3661348
You MRen BHan XZhou H(2024)Cross-Factory Polarizer Sheet Surface Defect Inspection System Based on Multiteacher Knowledge AmalgamationIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.341759673(1-16)Online publication date: 2024
https://doi.org/10.1109/TIM.2024.3417596

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cover image ACM Conferences

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

October 2023

5508 pages

ISBN:9798400701245

DOI:10.1145/3583780

General Chairs:
Ingo Frommholz
University of Wolverhampton, UK
,
Frank Hopfgartner
University of Koblenz, Germany
,
Mark Lee
University of Birmingham, UK
,
Michael Oakes
University of Birmingham, UK
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Min Zhang
Tsinghua University, China
,
Rodrygo Santos
Federal University of Minas Gerais, Brazil

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Published: 21 October 2023

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CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management

October 21 - 25, 2023

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Cited By

Hou CZhou YCao YLiu THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)ECAT: A Entire space Continual and Adaptive Transfer Learning Framework for Cross-Domain RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661348(2885-2889)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3661348
You MRen BHan XZhou H(2024)Cross-Factory Polarizer Sheet Surface Defect Inspection System Based on Multiteacher Knowledge AmalgamationIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.341759673(1-16)Online publication date: 2024
https://doi.org/10.1109/TIM.2024.3417596

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