Source-free unsupervised domain adaptation: A survey

Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention for tackling domain-shift problems caused by distribution discrepancy across different domains. Existing UDA approaches highly depend on the accessibility of source domain data, which is usually limited in practical scenarios due to privacy protection, data storage and transmission cost, and computation burden. To tackle this issue, many source-free unsupervised domain adaptation (SFUDA) methods have been proposed recently, which perform knowledge transfer from a pre-trained source model to the unlabeled target domain with source data inaccessible. A comprehensive review of these works on SFUDA is of great significance. In this paper, we provide a timely and systematic literature review of existing SFUDA approaches from a technical perspective. Specifically, we categorize current SFUDA studies into two groups, i.e., white-box SFUDA and black-box SFUDA, and further divide them into finer subcategories based on different learning strategies they use. We also investigate the challenges of methods in each subcategory, discuss the advantages/disadvantages of white-box and black-box SFUDA methods, conclude the commonly used benchmark datasets, and summarize the popular techniques for improved generalizability of models learned without using source data. We finally discuss several promising future directions in this field.

Superpixel-guided class-level denoising for unsupervised domain adaptive fundus image segmentation without source data

Unsupervised domain adaptation (UDA), which is used to alleviate the domain shift between the source domain and target domain, has attracted substantial research interest. Previous studies have proposed effective UDA methods which require both labeled source data and unlabeled target data to achieve desirable distribution alignment. However, due to privacy concerns, the vendor side often can only trade the pretrained source model without providing the source data to the targeted client, leading to failed adaptation by classical UDA techniques. To address this issue, in this paper, a novel Superpixel-guided Class-level Denoised self-training framework (SCD) is proposed, aiming at effectively adapting the pretrained source model to the target domain in the absence of source data. Since the source data is unavailable, the model can only be trained on the target domain with the pseudo labels obtained from the pretrained source model. However, due to domain shift, the predictions obtained by the source model on the target domain are noisy. Considering this, we propose three mutual-reinforcing components tailored to our self-training framework: (i) an adaptive class-aware thresholding strategy for more balanced pseudo label generation, (ii) a masked superpixel-guided clustering method for generating multiple content-adaptive and spatial-adaptive feature centroids that enhance the discriminability of final prototypes for effective prototypical label denoising, and (iii) adaptive learning schemes for suspected noisy-labeled and correct-labeled pixels to effectively utilize the valuable information available. Comprehensive experiments on multi-site fundus image segmentation demonstrate the superior performance of our approach and the effectiveness of each component.