Source Free Domain Adaptation with Image Translation
Yunzhong Hou, Liang Zheng

TL;DR
This paper introduces a source-free domain adaptation method that uses image translation to align target image styles with source styles, improving classification accuracy without access to source data.
Contribution
It proposes a novel style transfer approach for source-free domain adaptation that aligns feature statistics to adapt pre-trained models to new target domains.
Findings
Consistent and statistically significant accuracy improvements on multiple datasets.
Effective style transfer enhances pre-trained model performance in target domains.
Method outperforms existing source-free domain adaptation techniques.
Abstract
Effort in releasing large-scale datasets may be compromised by privacy and intellectual property considerations. A feasible alternative is to release pre-trained models instead. While these models are strong on their original task (source domain), their performance might degrade significantly when deployed directly in a new environment (target domain), which might not contain labels for training under realistic settings. Domain adaptation (DA) is a known solution to the domain gap problem, but usually requires labeled source data. In this paper, we study the problem of source free domain adaptation (SFDA), whose distinctive feature is that the source domain only provides a pre-trained model, but no source data. Being source free adds significant challenges to DA, especially when considering that the target dataset is unlabeled. To solve the SFDA problem, we propose an image translation…
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Taxonomy
TopicsDomain Adaptation and Few-Shot Learning · Multimodal Machine Learning Applications · Advanced Neural Network Applications
MethodsBatch Normalization
