Universal Source-Free Domain Adaptation

Kundu, Jogendra Nath; Venkat, Naveen; Rahul, M; Babu, R. Venkatesh

doi:10.1109/cvpr42600.2020.00460

Cited by 151 publications

(134 citation statements)

References 28 publications

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“…A few recent papers tackle model adaptation for classification problems [44,42,16]. [39] proposes source-free domain adaptation in the case where label knowledge of the target domain is not available, and show their efficiency on a set of classification problems with varying levels of label overlap. As we argued in Section 1, to the best of our knowledge, this problem has not been tackled in the context of semantic segmentation.…”

Section: Related Workmentioning

confidence: 99%

Uncertainty Reduction for Model Adaptation in Semantic Segmentation

Fleuret

2021

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Section: Related Workmentioning

confidence: 99%

Uncertainty Reduction for Model Adaptation in Semantic Segmentation

Fleuret

2021

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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“…At the same time, information maximization principle is used such that the correct classification probability in the target domain should be close to one-hot encodings. The solution to the source data-free problem of USF 26 is also minimizing the entropy similar to SHOT. Further, SFDA uses prototypes and filtering mechanism to improve the reliability of the pseudo labels.…”

Section: Source Data-free Domain Adaptationmentioning

confidence: 99%

“…Only few recent works studied source data-free domain adaptation in the following three categories: The first one adjusts the source model by self-supervised learning based on pseudo labels [25][26][27] ; the second one is to generate some target domain style samples which can be correctly classified by the source model 28 ; and the final is to transform the target domain samples to the source domain style samples. 29 First of all, without source domain data, it is not easy to do satisfied image translation between the target and source domains.…”

Section: Introductionmentioning

confidence: 99%

Source data‐free domain adaptation of object detector through domain‐specific perturbation

Xiong

Zhang

et al. 2021

Int J Intell Syst

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The current unsupervised cross‐domain detection methods need source domain data to retrain the detection model in target domain. However, the source domain data may be unavailable due to privacy, decentralization, or computation resource restrictions. A natural idea is to optimize the parameters of the source domain model by self‐supervised learning based on pseudo labels. We propose another approach from the viewpoint of noise perturbation without pseudo‐labeling. It can be assumed that the source and target domains are actually derived from a domain invariant space through domain‐specific perturbations, respectively. A super target domain can be constructed by augmenting more target domain perturbations to the target domain images. The optimal direction of the target domain to the domain invariant space can be approximated as the alignment direction from the super target domain to the target domain. Based on this idea, we propose a novel method called SOAP (SOurce data‐free domain Adaptation through domain Perturbation) which can remove domain perturbation from the target domain. The image‐level, instance‐level, and category consistency regularizations based on Mean Teacher structure are proposed to learn the correct alignment direction. Specifically, the category consistency can also further improve the classification accuracy. Extensive experiments on multiple domain adaptation scenarios demonstrate that SOAP achieves better performance surpassing the baseline (Faster R‐CNN) and multiple state‐of‐the‐art domain adaptation methods which need to access source domain data.

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“…Unlike conventional DA, one can not get access to the source domain data in source-free DA but is provided a model trained on the source domain data. About source-free DA in computer vision, several approaches have been proposed; (Sahoo et al, 2020) assumes the target domain data is a transformation from the source domain data along natural axes such as brightness and contrast; (Kundu et al, 2020) proposes universal DA that is trained via two-stage learning of procurement and deployment; (Kim et al, 2020) progressively updates the target model with pseudo-labels which are selected under self-entropy criterion.…”

Section: Introductionmentioning

confidence: 99%

Self-Adapter at SemEval-2021 Task 10: Entropy-based Pseudo-Labeler for Source-free Domain Adaptation

Yoon¹,

Kim

Jung

2021

Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

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Source-free domain adaptation is an emerging line of work in deep learning research since it is closely related to the real-world environment. We study the domain adaption in the sequence labeling problem where the model trained on the source domain data is given. We propose two methods: Self-Adapter and Selective Classifier Training. Self-Adapter is a training method that uses sentence-level pseudolabels filtered by the self-entropy threshold to provide supervision to the whole model. Selective Classifier Training uses token-level pseudo-labels and supervises only the classification layer of the model.The proposed methods are evaluated on data provided by SemEval-2021 task 10 and Self-Adapter achieves 2 nd rank performance.

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Universal Source-Free Domain Adaptation

Cited by 151 publications

References 28 publications

Uncertainty Reduction for Model Adaptation in Semantic Segmentation

Uncertainty Reduction for Model Adaptation in Semantic Segmentation

Source data‐free domain adaptation of object detector through domain‐specific perturbation

Self-Adapter at SemEval-2021 Task 10: Entropy-based Pseudo-Labeler for Source-free Domain Adaptation

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