You only label once: A self‐adaptive clustering‐based method for source‐free active domain adaptation
Abstract With the growing significance of data privacy protection, Source‐Free Domain Adaptation (SFDA) has gained attention as a research topic that aims to transfer knowledge from a labeled source domain to an unlabeled target domain without accessing source data. However, the absence of source da...
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Format: | Article |
Language: | English |
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Wiley
2024-04-01
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Series: | IET Image Processing |
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Online Access: | https://doi.org/10.1049/ipr2.13025 |
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author | Zhishu Sun Luojun Lin Yuanlong Yu |
author_facet | Zhishu Sun Luojun Lin Yuanlong Yu |
author_sort | Zhishu Sun |
collection | DOAJ |
description | Abstract With the growing significance of data privacy protection, Source‐Free Domain Adaptation (SFDA) has gained attention as a research topic that aims to transfer knowledge from a labeled source domain to an unlabeled target domain without accessing source data. However, the absence of source data often leads to model collapse or restricts the performance improvements of SFDA methods, as there is insufficient true‐labeled knowledge for each category. To tackle this, Source‐Free Active Domain Adaptation (SFADA) has emerged as a new task that aims to improve SFDA by selecting a small set of informative target samples labeled by experts. Nevertheless, existing SFADA methods impose a significant burden on human labelers, requiring them to continuously label a substantial number of samples throughout the training period. In this paper, a novel approach is proposed to alleviate the labeling burden in SFADA by only necessitating the labeling of an extremely small number of samples on a one‐time basis. Moreover, considering the inherent sparsity of these selected samples in the target domain, a Self‐adaptive Clustering‐based Active Learning (SCAL) method is proposed that propagates the labels of selected samples to other datapoints within the same cluster. To further enhance the accuracy of SCAL, a self‐adaptive scale search method is devised that automatically determines the optimal clustering scale, using the entropy of the entire target dataset as a guiding criterion. The experimental evaluation presents compelling evidence of our method's supremacy. Specifically, it outstrips previous SFDA methods, delivering state‐of‐the‐art (SOTA) results on standard benchmarks. Remarkably, it accomplishes this with less than 0.5% annotation cost, in stark contrast to the approximate 5% required by earlier techniques. The approach thus not only sets new performance benchmarks but also offers a markedly more practical and cost‐effective solution for SFADA, making it an attractive choice for real‐world applications where labeling resources are limited. |
first_indexed | 2024-04-24T11:53:48Z |
format | Article |
id | doaj.art-dff4ce55e7c14e318ed3e3d69abdda4b |
institution | Directory Open Access Journal |
issn | 1751-9659 1751-9667 |
language | English |
last_indexed | 2024-04-24T11:53:48Z |
publishDate | 2024-04-01 |
publisher | Wiley |
record_format | Article |
series | IET Image Processing |
spelling | doaj.art-dff4ce55e7c14e318ed3e3d69abdda4b2024-04-09T06:07:10ZengWileyIET Image Processing1751-96591751-96672024-04-011851268128210.1049/ipr2.13025You only label once: A self‐adaptive clustering‐based method for source‐free active domain adaptationZhishu Sun0Luojun Lin1Yuanlong Yu2College of Computer and Data Science Fuzhou University Fuzhou ChinaCollege of Computer and Data Science Fuzhou University Fuzhou ChinaCollege of Computer and Data Science Fuzhou University Fuzhou ChinaAbstract With the growing significance of data privacy protection, Source‐Free Domain Adaptation (SFDA) has gained attention as a research topic that aims to transfer knowledge from a labeled source domain to an unlabeled target domain without accessing source data. However, the absence of source data often leads to model collapse or restricts the performance improvements of SFDA methods, as there is insufficient true‐labeled knowledge for each category. To tackle this, Source‐Free Active Domain Adaptation (SFADA) has emerged as a new task that aims to improve SFDA by selecting a small set of informative target samples labeled by experts. Nevertheless, existing SFADA methods impose a significant burden on human labelers, requiring them to continuously label a substantial number of samples throughout the training period. In this paper, a novel approach is proposed to alleviate the labeling burden in SFADA by only necessitating the labeling of an extremely small number of samples on a one‐time basis. Moreover, considering the inherent sparsity of these selected samples in the target domain, a Self‐adaptive Clustering‐based Active Learning (SCAL) method is proposed that propagates the labels of selected samples to other datapoints within the same cluster. To further enhance the accuracy of SCAL, a self‐adaptive scale search method is devised that automatically determines the optimal clustering scale, using the entropy of the entire target dataset as a guiding criterion. The experimental evaluation presents compelling evidence of our method's supremacy. Specifically, it outstrips previous SFDA methods, delivering state‐of‐the‐art (SOTA) results on standard benchmarks. Remarkably, it accomplishes this with less than 0.5% annotation cost, in stark contrast to the approximate 5% required by earlier techniques. The approach thus not only sets new performance benchmarks but also offers a markedly more practical and cost‐effective solution for SFADA, making it an attractive choice for real‐world applications where labeling resources are limited.https://doi.org/10.1049/ipr2.13025computer visionimage recognition |
spellingShingle | Zhishu Sun Luojun Lin Yuanlong Yu You only label once: A self‐adaptive clustering‐based method for source‐free active domain adaptation IET Image Processing computer vision image recognition |
title | You only label once: A self‐adaptive clustering‐based method for source‐free active domain adaptation |
title_full | You only label once: A self‐adaptive clustering‐based method for source‐free active domain adaptation |
title_fullStr | You only label once: A self‐adaptive clustering‐based method for source‐free active domain adaptation |
title_full_unstemmed | You only label once: A self‐adaptive clustering‐based method for source‐free active domain adaptation |
title_short | You only label once: A self‐adaptive clustering‐based method for source‐free active domain adaptation |
title_sort | you only label once a self adaptive clustering based method for source free active domain adaptation |
topic | computer vision image recognition |
url | https://doi.org/10.1049/ipr2.13025 |
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