Domain Adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain whose data distributions are different. However, the training data in source domain required by most of the existing methods is usually unavailable in real-world applications due to privacy preserving policies. 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. Firstly, we produce robust pseudo-labels for target data with spherical k-means clustering, whose initial class centers are the weight vectors (anchors) learned by the classifier of pretrained model. Furthermore, we propose to estimate the class-conditioned feature distribution of source domain by exploiting target data and corresponding anchors. Finally, we sample surrogate features from the estimated distribution, which are then utilized to align two domains by minimizing a contrastive adaptation loss function. Extensive experiments show that the proposed method achieves state-of-the-art performance on multiple DA benchmarks, and even outperforms traditional DA methods which require plenty of source data.

翻译：域适应旨在将从标签源域中获取的知识转移到数据分布不同的未标签目标域;然而,由于隐私保护政策,大多数现有方法要求的源域培训数据通常在现实应用中无法获得;最近,无源域适应(SFDA)引起很大注意,它试图在不使用源数据的情况下解决域适应问题;在这项工作中,我们提议了一个名为SFDA-DE的新框架,通过源分配估计来应对SFDA任务。首先,我们为球形K-海量组合的目标数据制作了强有力的假标签,其初始类中心是预先培训模型的分类所学的重量矢量(锚)。此外,我们提议通过利用目标数据和相应的锚来估计源域的等级限制特征分布。最后,我们从估计分布中抽取了假定特征,然后通过尽量减少对比性适应损失功能来将这两个领域相匹配。广泛的实验显示,拟议的方法在多种DA基准中达到了最先进的性性能,甚至超越了传统源数据。