Standard Unsupervised Domain Adaptation (UDA) methods assume the availability
of both source and target data during the adaptation. In this work, we
investigate Source-free Unsupervised Domain Adaptation (SF-UDA), a specific
case of UDA where a model is adapted to a target domain without access to
source data. We propose a novel approach for the SF-UDA setting based on a loss
reweighting strategy that brings robustness against the noise that inevitably
affects the pseudo-labels. The classification loss is reweighted based on the
reliability of the pseudo-labels that is measured by estimating their
uncertainty. Guided by such reweighting strategy, the pseudo-labels are
progressively refined by aggregating knowledge from neighbouring samples.
Furthermore, a self-supervised contrastive framework is leveraged as a target
space regulariser to enhance such knowledge aggregation. A novel negative pairs
exclusion strategy is proposed to identify and exclude negative pairs made of
samples sharing the same class, even in presence of some noise in the
pseudo-labels. Our method outperforms previous methods on three major
benchmarks by a large margin. We set the new SF-UDA state-of-the-art on VisDA-C
and DomainNet with a performance gain of +1.8% on both benchmarks and on PACS
with +12.3% in the single-source setting and +6.6% in multi-target adaptation.
Additional analyses demonstrate that the proposed approach is robust to the
noise, which results in significantly more accurate pseudo-labels compared to
state-of-the-art approaches.

本文提出一种基于损失加权策略的 Source-free Unsupervised Domain Adaptation 方法，该方法在没有源数据的情况下，通过估计伪标签的不确定性来逐步优化伪标签，并采用一种自监督对比框架作为目标空间规范化器来增强知识聚合，并在三个基准测试中均取得了显著的优异表现，证明了该方法的鲁棒性。

基于不确定性估计的伪标签引导源无关非监督域自适应

Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation

3D object detectors based only on LiDAR point clouds hold the
state-of-the-art on modern street-view benchmarks. However, LiDAR-based
detectors poorly generalize across domains due to domain shift. In the case of
LiDAR, in fact, domain shift is not only due to changes in the environment and
in the object appearances, as for visual data from RGB cameras, but is also
related to the geometry of the point clouds (e.g., point density variations).
This paper proposes SF-UDA$^{3D}$, the first Source-Free Unsupervised Domain
Adaptation (SF-UDA) framework to domain-adapt the state-of-the-art PointRCNN 3D
detector to target domains for which we have no annotations (unsupervised),
neither we hold images nor annotations of the source domain (source-free).
SF-UDA$^{3D}$ is novel on both aspects. Our approach is based on
pseudo-annotations, reversible scale-transformations and motion coherency.
SF-UDA$^{3D}$ outperforms both previous domain adaptation techniques based on
features alignment and state-of-the-art 3D object detection methods which
additionally use few-shot target annotations or target annotation statistics.
This is demonstrated by extensive experiments on two large-scale datasets,
i.e., KITTI and nuScenes.

本文提出了 SF-UDA^3D 框架，基于伪标注、可逆的尺度转换和运动相干性，将最先进的 PointRCNN 3D 检测器域自适应到无注释目标域中，结果在 KITTI 和 nuScenes 上都优于以前的特征对齐方法和最先进的 3D 目标检测方法。