Source-free unsupervised domain adaptation (SFUDA) aims to enable the
utilization of a pre-trained source model in an unlabeled target domain without
access to source data. Self-training is a way to solve SFUDA, where confident
target samples are iteratively selected as pseudo-labeled samples to guide
target model learning. However, prior heuristic noisy pseudo-label filtering
methods all involve introducing extra models, which are sensitive to model
assumptions and may introduce additional errors or mislabeling. In this work,
we propose a method called Uncertainty-aware Pseudo-label-filtering Adaptation
(UPA) to efficiently address this issue in a coarse-to-fine manner. Specially,
we first introduce a sample selection module named Adaptive Pseudo-label
Selection (APS), which is responsible for filtering noisy pseudo labels. The
APS utilizes a simple sample uncertainty estimation method by aggregating
knowledge from neighboring samples and confident samples are selected as clean
pseudo-labeled. Additionally, we incorporate Class-Aware Contrastive Learning
(CACL) to mitigate the memorization of pseudo-label noise by learning robust
pair-wise representation supervised by pseudo labels. Through extensive
experiments conducted on three widely used benchmarks, we demonstrate that our
proposed method achieves competitive performance on par with state-of-the-art
SFUDA methods. Code is available at this https URL

无源无监督域自适应的不确定性伪标签过滤适应（UPA）方法，通过自适应伪标签选择（APS）模块进行精确过滤，同时引入类感知对比学习（CACL）以降低伪标签噪声的记忆化，实现与最先进的方法相当的竞争性能。

基于不确定性感知的无源无监督域适应伪标签过滤

Uncertainty-Aware Pseudo-Label Filtering for Source-Free Unsupervised  Domain Adaptation

Pseudo-label (PL) filtering forms a crucial part of Self-Training (ST)
methods for unsupervised domain adaptation. Dropout-based Uncertainty-driven
Self-Training (DUST) proceeds by first training a teacher model on source
domain labeled data. Then, the teacher model is used to provide PLs for the
unlabeled target domain data. Finally, we train a student on augmented labeled
and pseudo-labeled data. The process is iterative, where the student becomes
the teacher for the next DUST iteration. A crucial step that precedes the
student model training in each DUST iteration is filtering out noisy PLs that
could lead the student model astray. In DUST, we proposed a simple, effective,
and theoretically sound PL filtering strategy based on the teacher model's
uncertainty about its predictions on unlabeled speech utterances. We estimate
the model's uncertainty by computing disagreement amongst multiple samples
drawn from the teacher model during inference by injecting noise via dropout.
In this work, we show that DUST's PL filtering, as initially used, may fail
under severe source and target domain mismatch. We suggest several approaches
to eliminate or alleviate this issue. Further, we bring insights from the
research in neural network model calibration to DUST and show that a
well-calibrated model correlates strongly with a positive outcome of the DUST
PL filtering step.

本文介绍了一种基于 Dropout 的不确定性驱动自训练（DUST）方法，该方法使用教师模型对未标记目标域数据提供伪标签。我们提出了一种基于模型预测的不确定性的简单，有效和理论上可靠的 PL 过滤策略，并指出 PL 过滤可能会在严重的源域和目标域不匹配的情况下失败。此外，我们还将神经网络模型校准的研究成果带到了 DUST 中，并发现模型校准与 DUST PL 过滤步骤的积极结果强相关。