The goal of test-time adaptation is to adapt a source-pretrained model to a
continuously changing target domain without relying on any source data.
Typically, this is either done by updating the parameters of the model (model
adaptation) using inputs from the target domain or by modifying the inputs
themselves (input adaptation). However, methods that modify the model suffer
from the issue of compounding noisy updates whereas methods that modify the
input need to adapt to every new data point from scratch while also struggling
with certain domain shifts. We introduce an approach that leverages a
pre-trained diffusion model to project the target domain images closer to the
source domain and iteratively updates the model via pseudo-label ensembling.
Our method combines the advantages of model and input adaptations while
mitigating their shortcomings. Our experiments on CIFAR-10C demonstrate the
superiority of our approach, outperforming the strongest baseline by an average
of 1.7% across 15 diverse corruptions and surpassing the strongest input
adaptation baseline by an average of 18%.

利用预训练的扩散模型将目标领域图像投影到源领域，并通过伪标签集成迭代更新模型的方法，将模型适应和输入适应的优势相结合，从而减轻它们的缺点。在 CIFAR-10C 上的实验表明，我们的方法优于最强基线平均 1.7％，比最强的输入适应基线平均高出 18％。

降低噪声：通过伪标签集成利用扩散模型进行测试时自适应

Turn Down the Noise: Leveraging Diffusion Models for Test-time  Adaptation via Pseudo-label Ensembling

Video action segmentation under timestamp supervision has recently received
much attention due to lower annotation costs. Most existing methods generate
pseudo-labels for all frames in each video to train the segmentation model.
However, these methods suffer from incorrect pseudo-labels, especially for the
semantically unclear frames in the transition region between two consecutive
actions, which we call ambiguous intervals. To address this issue, we propose a
novel framework from the perspective of clustering, which includes the
following two parts. First, pseudo-label ensembling generates incomplete but
high-quality pseudo-label sequences, where the frames in ambiguous intervals
have no pseudo-labels. Second, iterative clustering iteratively propagates the
pseudo-labels to the ambiguous intervals by clustering, and thus updates the
pseudo-label sequences to train the model. We further introduce a clustering
loss, which encourages the features of frames within the same action segment
more compact. Extensive experiments show the effectiveness of our method.

本文从聚类的角度提出了一种框架来解决视频动作分割中矛盾间隙带来的错误伪标签问题，并引入了聚类损失函数，使得相同动作段内的帧特征更加紧凑，实验结果表明该方法有效。