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.

本文提出了基于源分布估计方法的 Source-Free Domain Adaptation 模型 SFDA-DE，采用球形 k 均值聚类生成目标数据的假伪标签，并利用目标数据和锚点学习源域的类条件特征分布，通过最小化交叉适应损失函数来对齐两个数据域，在多个 DA 基准测试中实现了最先进的性能表现，并且优于需要大量源数据的传统 DA 方法。