The objective of Continual Test-time Domain Adaptation (CTDA) is to gradually
adapt a pre-trained model to a sequence of target domains without accessing the
source data. This paper proposes a Dynamic Sample Selection (DSS) method for
CTDA. DSS consists of dynamic thresholding, positive learning, and negative
learning processes. Traditionally, models learn from unlabeled unknown
environment data and equally rely on all samples' pseudo-labels to update their
parameters through self-training. However, noisy predictions exist in these
pseudo-labels, so all samples are not equally trustworthy. Therefore, in our
method, a dynamic thresholding module is first designed to select suspected
low-quality from high-quality samples. The selected low-quality samples are
more likely to be wrongly predicted. Therefore, we apply joint positive and
negative learning on both high- and low-quality samples to reduce the risk of
using wrong information. We conduct extensive experiments that demonstrate the
effectiveness of our proposed method for CTDA in the image domain,
outperforming the state-of-the-art results. Furthermore, our approach is also
evaluated in the 3D point cloud domain, showcasing its versatility and
potential for broader applicability.

在该文中，作者提出了一种用于 Continual Test-time Domain Adaptation 的 Dynamic Sample Selection（DSS）方法，通过动态阈值、正向学习和反向学习等过程，选择低质量样本并进行联合学习，从而提高模型在图像领域的效果，并展示了其在 3D 点云领域的适用性和潜力。

连续化测试时间域自适应通过动态样本选择

Continual Test-time Domain Adaptation via Dynamic Sample Selection

Domain adaptive semantic segmentation is recognized as a promising technique
to alleviate the domain shift between the labeled source domain and the
unlabeled target domain in many real-world applications, such as automatic
pilot. However, large amounts of source domain data often introduce significant
costs in storage and training, and sometimes the source data is inaccessible
due to privacy policies. To address these problems, we investigate domain
adaptive semantic segmentation without source data, which assumes that the
model is pre-trained on the source domain, and then adapting to the target
domain without accessing source data anymore. Since there is no supervision
from the source domain data, many self-training methods tend to fall into the
``winner-takes-all'' dilemma, where the {\it majority} classes totally dominate
the segmentation networks and the networks fail to classify the {\it minority}
classes. Consequently, we propose an effective framework for this challenging
problem with two components: positive learning and negative learning. In
positive learning, we select the class-balanced pseudo-labeled pixels with
intra-class threshold, while in negative learning, for each pixel, we
investigate which category the pixel does not belong to with the proposed
heuristic complementary label selection. Notably, our framework can be easily
implemented and incorporated with other methods to further enhance the
performance. Extensive experiments on two widely-used synthetic-to-real
benchmarks demonstrate our claims and the effectiveness of our framework, which
outperforms the baseline with a large margin. Code is available at
https://github.com/fumyou13/LDBE.

一个基于自训练网络的语义分割框架，该框架主要包含两个部分：正样本提取和负样本提取，以及选用了启发式的互补标签选择方法。通过实验可知，该框架在广泛应用的合成到实际领域的语义分割任务上表现出了很高的性能。

领域自适应语义分割无源数据

Domain Adaptive Semantic Segmentation without Source Data

Training of Convolutional Neural Networks (CNNs) with data with noisy labels
is known to be a challenge. Based on the fact that directly providing the label
to the data (Positive Learning; PL) has a risk of allowing CNNs to memorize the
contaminated labels for the case of noisy data, the indirect learning approach
that uses complementary labels (Negative Learning for Noisy Labels; NLNL) has
proven to be highly effective in preventing overfitting to noisy data as it
reduces the risk of providing faulty target. NLNL further employs a three-stage
pipeline to improve convergence. As a result, filtering noisy data through the
NLNL pipeline is cumbersome, increasing the training cost. In this study, we
propose a novel improvement of NLNL, named Joint Negative and Positive Learning
(JNPL), that unifies the filtering pipeline into a single stage. JNPL trains
CNN via two losses, NL+ and PL+, which are improved upon NL and PL loss
functions, respectively. We analyze the fundamental issue of NL loss function
and develop new NL+ loss function producing gradient that enhances the
convergence of noisy data. Furthermore, PL+ loss function is designed to enable
faster convergence to expected-to-be-clean data. We show that the NL+ and PL+
train CNN simultaneously, significantly simplifying the pipeline, allowing
greater ease of practical use compared to NLNL. With a simple semi-supervised
training technique, our method achieves state-of-the-art accuracy for noisy
data classification based on the superior filtering ability.

本文介绍了一种新颖的基于联合消极和积极学习的方法 (JNPL)，该方法通过两个损失函数 (NL + 和 PL+) 对 CNN 进行训练，并通过简化 pipeline 过程实现对带有噪声标签的数据的高效分类任务。