Source-Free Domain Adaptation for Semantic Segmentation
- URL: http://arxiv.org/abs/2103.16372v1
- Date: Tue, 30 Mar 2021 14:14:29 GMT
- Title: Source-Free Domain Adaptation for Semantic Segmentation
- Authors: Yuang Liu, Wei Zhang, Jun Wang
- Abstract summary: Unsupervised Domain Adaptation (UDA) can tackle the challenge that convolutional neural network-based approaches for semantic segmentation heavily rely on the pixel-level annotated data.
We propose a source-free domain adaptation framework for semantic segmentation, namely SFDA, in which only a well-trained source model and an unlabeled target domain dataset are available for adaptation.
- Score: 11.722728148523366
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Unsupervised Domain Adaptation (UDA) can tackle the challenge that
convolutional neural network(CNN)-based approaches for semantic segmentation
heavily rely on the pixel-level annotated data, which is labor-intensive.
However, existing UDA approaches in this regard inevitably require the full
access to source datasets to reduce the gap between the source and target
domains during model adaptation, which are impractical in the real scenarios
where the source datasets are private, and thus cannot be released along with
the well-trained source models. To cope with this issue, we propose a
source-free domain adaptation framework for semantic segmentation, namely SFDA,
in which only a well-trained source model and an unlabeled target domain
dataset are available for adaptation. SFDA not only enables to recover and
preserve the source domain knowledge from the source model via knowledge
transfer during model adaptation, but also distills valuable information from
the target domain for self-supervised learning. The pixel- and patch-level
optimization objectives tailored for semantic segmentation are seamlessly
integrated in the framework. The extensive experimental results on numerous
benchmark datasets highlight the effectiveness of our framework against the
existing UDA approaches relying on source data.
Related papers
- Open-Set Domain Adaptation with Visual-Language Foundation Models [51.49854335102149]
Unsupervised domain adaptation (UDA) has proven to be very effective in transferring knowledge from a source domain to a target domain with unlabeled data.
Open-set domain adaptation (ODA) has emerged as a potential solution to identify these classes during the training phase.
arXiv Detail & Related papers (2023-07-30T11:38:46Z) - Towards Source-free Domain Adaptive Semantic Segmentation via Importance-aware and Prototype-contrast Learning [26.544837987747766]
We propose an end-to-end source-free domain adaptation semantic segmentation method via Importance-Aware and Prototype-Contrast learning.
The proposed IAPC framework effectively extracts domain-invariant knowledge from the well-trained source model and learns domain-specific knowledge from the unlabeled target domain.
arXiv Detail & Related papers (2023-06-02T15:09:19Z) - Continual Source-Free Unsupervised Domain Adaptation [37.060694803551534]
Existing Source-free Unsupervised Domain Adaptation approaches exhibit catastrophic forgetting.
We propose a Continual SUDA (C-SUDA) framework to cope with the challenge of SUDA in a continual learning setting.
arXiv Detail & Related papers (2023-04-14T20:11:05Z) - Source-Free Domain Adaptation via Distribution Estimation [106.48277721860036]
Domain Adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain whose data distributions are different.
Recently, Source-Free Domain Adaptation (SFDA) has drawn much attention, which tries to tackle domain adaptation problem without using source data.
In this work, we propose a novel framework called SFDA-DE to address SFDA task via source Distribution Estimation.
arXiv Detail & Related papers (2022-04-24T12:22:19Z) - Instance Relation Graph Guided Source-Free Domain Adaptive Object
Detection [79.89082006155135]
Unsupervised Domain Adaptation (UDA) is an effective approach to tackle the issue of domain shift.
UDA methods try to align the source and target representations to improve the generalization on the target domain.
The Source-Free Adaptation Domain (SFDA) setting aims to alleviate these concerns by adapting a source-trained model for the target domain without requiring access to the source data.
arXiv Detail & Related papers (2022-03-29T17:50:43Z) - Source-Free Domain Adaptive Fundus Image Segmentation with Denoised
Pseudo-Labeling [56.98020855107174]
Domain adaptation typically requires to access source domain data to utilize their distribution information for domain alignment with the target data.
In many real-world scenarios, the source data may not be accessible during the model adaptation in the target domain due to privacy issue.
We present a novel denoised pseudo-labeling method for this problem, which effectively makes use of the source model and unlabeled target data.
arXiv Detail & Related papers (2021-09-19T06:38:21Z) - Adapting Off-the-Shelf Source Segmenter for Target Medical Image
Segmentation [12.703234995718372]
Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled source domain to an unlabeled and unseen target domain.
Access to the source domain data at the adaptation stage is often limited, due to data storage or privacy issues.
We propose to adapt an off-the-shelf" segmentation model pre-trained in the source domain to the target domain.
arXiv Detail & Related papers (2021-06-23T16:16:55Z) - Unsupervised Model Adaptation for Continual Semantic Segmentation [15.820660013260584]
We develop an algorithm for adapting a semantic segmentation model that is trained using a labeled source domain to generalize well in an unlabeled target domain.
We provide theoretical analysis and explain conditions under which our algorithm is effective.
Experiments on benchmark adaptation task demonstrate our method achieves competitive performance even compared with joint UDA approaches.
arXiv Detail & Related papers (2020-09-26T04:55:50Z) - Towards Inheritable Models for Open-Set Domain Adaptation [56.930641754944915]
We introduce a practical Domain Adaptation paradigm where a source-trained model is used to facilitate adaptation in the absence of the source dataset in future.
We present an objective way to quantify inheritability to enable the selection of the most suitable source model for a given target domain, even in the absence of the source data.
arXiv Detail & Related papers (2020-04-09T07:16:30Z) - Do We Really Need to Access the Source Data? Source Hypothesis Transfer
for Unsupervised Domain Adaptation [102.67010690592011]
Unsupervised adaptationUDA (UDA) aims to leverage the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled domain.
Prior UDA methods typically require to access the source data when learning to adapt the model.
This work tackles a practical setting where only a trained source model is available and how we can effectively utilize such a model without source data to solve UDA problems.
arXiv Detail & Related papers (2020-02-20T03:13:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.