{"title":"Domain Adversarial Active Learning for Domain Generalization Classification","authors":"Jianting Chen;Ling Ding;Yunxiao Yang;Zaiyuan Di;Yang Xiang","doi":"10.1109/TKDE.2024.3486204","DOIUrl":null,"url":null,"abstract":"Domain generalization (DG) tasks aim to learn cross-domain models from source domains and apply them to unknown target domains. Recent research has demonstrated that diverse and rich source domain samples can enhance domain generalization capability. This work argues that the impact of each sample on the model's generalization ability varies. Even a small-scale but high-quality dataset can achieve a notable level of generalization. Motivated by this, we propose a domain-adversarial active learning (DAAL) algorithm for classification tasks in DG. First, we analyze that the objective of DG tasks is to maximize the inter-class distance within the same domain and minimize the intra-class distance across different domains. We design a domain adversarial selection method that prioritizes challenging samples in an active learning (AL) framework. Second, we hypothesize that even in a converged model, some feature subsets lack discriminatory power within each domain. We develop a method to identify and optimize these feature subsets, thereby maximizing inter-class distance of features. Lastly, We experimentally compare our DAAL algorithm with various DG and AL algorithms across four datasets. The results demonstrate that the DAAL algorithm can achieve strong generalization ability with fewer data resources, thereby significantly reducing data annotation costs in DG tasks.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 1","pages":"226-238"},"PeriodicalIF":8.9000,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Knowledge and Data Engineering","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10734227/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Domain generalization (DG) tasks aim to learn cross-domain models from source domains and apply them to unknown target domains. Recent research has demonstrated that diverse and rich source domain samples can enhance domain generalization capability. This work argues that the impact of each sample on the model's generalization ability varies. Even a small-scale but high-quality dataset can achieve a notable level of generalization. Motivated by this, we propose a domain-adversarial active learning (DAAL) algorithm for classification tasks in DG. First, we analyze that the objective of DG tasks is to maximize the inter-class distance within the same domain and minimize the intra-class distance across different domains. We design a domain adversarial selection method that prioritizes challenging samples in an active learning (AL) framework. Second, we hypothesize that even in a converged model, some feature subsets lack discriminatory power within each domain. We develop a method to identify and optimize these feature subsets, thereby maximizing inter-class distance of features. Lastly, We experimentally compare our DAAL algorithm with various DG and AL algorithms across four datasets. The results demonstrate that the DAAL algorithm can achieve strong generalization ability with fewer data resources, thereby significantly reducing data annotation costs in DG tasks.
期刊介绍:
The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.