Source-Free Progressive Graph Learning for Open-Set Domain Adaptation

Open-set domain adaptation (OSDA) aims to transfer knowledge from a label-rich source domain to a label-scarce target domain while addressing disturbances from irrelevant target classes not present in the source data. However, most OSDA approaches are limited due to the lack of essential theoretical...

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Vydáno v:	IEEE transactions on pattern analysis and machine intelligence Ročník 45; číslo 9; s. 11240 - 11255
Hlavní autoři:	Luo, Yadan, Wang, Zijian, Chen, Zhuoxiao, Huang, Zi, Baktashmotlagh, Mahsa
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.09.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Action recognition Adaptation Adaptation models domain adaptation Graph neural networks Hypotheses Image classification Knowledge management Labels Machine learning open-set domain adaptation Predictive models Semantics source-free domain adaptation Subspaces Task analysis Training Uncertainty Upper bounds
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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Abstract	Open-set domain adaptation (OSDA) aims to transfer knowledge from a label-rich source domain to a label-scarce target domain while addressing disturbances from irrelevant target classes not present in the source data. However, most OSDA approaches are limited due to the lack of essential theoretical analysis of generalization bound, reliance on the coexistence of source and target data during adaptation, and failure to accurately estimate model predictions' uncertainty. To address these limitations, the Progressive Graph Learning (PGL) framework is proposed. PGL decomposes the target hypothesis space into shared and unknown subspaces and progressively pseudo-labels the most confident known samples from the target domain for hypothesis adaptation. PGL guarantees a tight upper bound of the target error by integrating a graph neural network with episodic training and leveraging adversarial learning to close the gap between the source and target distributions. The proposed approach also tackles a more realistic source-free open-set domain adaptation (SF-OSDA) setting that makes no assumptions about the coexistence of source and target domains. In a two-stage framework, the SF-PGL model' uniformly selects the most confident target instances from each category at a fixed ratio, and the confidence thresholds in each class weigh the classification loss in the adaptation step. The proposed methods are evaluated on benchmark image classification and action recognition datasets, where they demonstrate superiority and flexibility in recognizing both shared and unknown categories. Additionally, balanced pseudo-labeling plays a significant role in improving calibration, making the trained model less prone to over- or under-confident predictions on the target data.
AbstractList	Open-set domain adaptation (OSDA) aims to transfer knowledge from a label-rich source domain to a label-scarce target domain while addressing disturbances from irrelevant target classes not present in the source data. However, most OSDA approaches are limited due to the lack of essential theoretical analysis of generalization bound, reliance on the coexistence of source and target data during adaptation, and failure to accurately estimate model predictions' uncertainty. To address these limitations, the Progressive Graph Learning (PGL) framework is proposed. PGL decomposes the target hypothesis space into shared and unknown subspaces and progressively pseudo-labels the most confident known samples from the target domain for hypothesis adaptation. PGL guarantees a tight upper bound of the target error by integrating a graph neural network with episodic training and leveraging adversarial learning to close the gap between the source and target distributions. The proposed approach also tackles a more realistic source-free open-set domain adaptation (SF-OSDA) setting that makes no assumptions about the coexistence of source and target domains. In a two-stage framework, the SF-PGL model' uniformly selects the most confident target instances from each category at a fixed ratio, and the confidence thresholds in each class weigh the classification loss in the adaptation step. The proposed methods are evaluated on benchmark image classification and action recognition datasets, where they demonstrate superiority and flexibility in recognizing both shared and unknown categories. Additionally, balanced pseudo-labeling plays a significant role in improving calibration, making the trained model less prone to over- or under-confident predictions on the target data. Open-set domain adaptation (OSDA) aims to transfer knowledge from a label-rich source domain to a label-scarce target domain while addressing disturbances from irrelevant target classes not present in the source data. However, most OSDA approaches are limited due to the lack of essential theoretical analysis of generalization bound, reliance on the coexistence of source and target data during adaptation, and failure to accurately estimate model predictions' uncertainty. To address these limitations, the Progressive Graph Learning (PGL) framework is proposed. PGL decomposes the target hypothesis space into shared and unknown subspaces and progressively pseudo-labels the most confident known samples from the target domain for hypothesis adaptation. PGL guarantees a tight upper bound of the target error by integrating a graph neural network with episodic training and leveraging adversarial learning to close the gap between the source and target distributions. The proposed approach also tackles a more realistic source-free open-set domain adaptation (SF-OSDA) setting that makes no assumptions about the coexistence of source and target domains. In a two-stage framework, the SF-PGL model' uniformly selects the most confident target instances from each category at a fixed ratio, and the confidence thresholds in each class weigh the classification loss in the adaptation step. The proposed methods are evaluated on benchmark image classification and action recognition datasets, where they demonstrate superiority and flexibility in recognizing both shared and unknown categories. Additionally, balanced pseudo-labeling plays a significant role in improving calibration, making the trained model less prone to over- or under-confident predictions on the target data.Open-set domain adaptation (OSDA) aims to transfer knowledge from a label-rich source domain to a label-scarce target domain while addressing disturbances from irrelevant target classes not present in the source data. However, most OSDA approaches are limited due to the lack of essential theoretical analysis of generalization bound, reliance on the coexistence of source and target data during adaptation, and failure to accurately estimate model predictions' uncertainty. To address these limitations, the Progressive Graph Learning (PGL) framework is proposed. PGL decomposes the target hypothesis space into shared and unknown subspaces and progressively pseudo-labels the most confident known samples from the target domain for hypothesis adaptation. PGL guarantees a tight upper bound of the target error by integrating a graph neural network with episodic training and leveraging adversarial learning to close the gap between the source and target distributions. The proposed approach also tackles a more realistic source-free open-set domain adaptation (SF-OSDA) setting that makes no assumptions about the coexistence of source and target domains. In a two-stage framework, the SF-PGL model' uniformly selects the most confident target instances from each category at a fixed ratio, and the confidence thresholds in each class weigh the classification loss in the adaptation step. The proposed methods are evaluated on benchmark image classification and action recognition datasets, where they demonstrate superiority and flexibility in recognizing both shared and unknown categories. Additionally, balanced pseudo-labeling plays a significant role in improving calibration, making the trained model less prone to over- or under-confident predictions on the target data.
Author	Luo, Yadan Chen, Zhuoxiao Huang, Zi Wang, Zijian Baktashmotlagh, Mahsa
Author_xml	– sequence: 1 givenname: Yadan orcidid: 0000-0001-6272-2971 surname: Luo fullname: Luo, Yadan email: y.luo@uq.edu.au organization: School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, QLD, Australia – sequence: 2 givenname: Zijian orcidid: 0000-0002-7190-9620 surname: Wang fullname: Wang, Zijian email: zijian.wang@uq.edu.au organization: School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, QLD, Australia – sequence: 3 givenname: Zhuoxiao orcidid: 0000-0001-5247-0109 surname: Chen fullname: Chen, Zhuoxiao email: zhuoxiao.chen@uq.edu.au organization: School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, QLD, Australia – sequence: 4 givenname: Zi orcidid: 0000-0002-9738-4949 surname: Huang fullname: Huang, Zi email: helen.huang@uq.edu.au organization: School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, QLD, Australia – sequence: 5 givenname: Mahsa orcidid: 0000-0001-5255-8194 surname: Baktashmotlagh fullname: Baktashmotlagh, Mahsa email: m.baktashmotlagh@uq.edu.au organization: School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, QLD, Australia
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SubjectTerms	Action recognition Adaptation Adaptation models domain adaptation Graph neural networks Hypotheses Image classification Knowledge management Labels Machine learning open-set domain adaptation Predictive models Semantics source-free domain adaptation Subspaces Task analysis Training Uncertainty Upper bounds
Title	Source-Free Progressive Graph Learning for Open-Set Domain Adaptation
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