Black-Box unsupervised domain adaptation (BBUDA) learns knowledge only with
the prediction of target data from the source model without access to the
source data and source model, which attempts to alleviate concerns about the
privacy and security of data. However, incorrect pseudo-labels are prevalent in
the prediction generated by the source model due to the cross-domain
discrepancy, which may substantially degrade the performance of the target
model. To address this problem, we propose a novel approach that incrementally
selects high-confidence pseudo-labels to improve the generalization ability of
the target model. Specifically, we first generate pseudo-labels using a source
model and train a crude target model by a vanilla BBUDA method. Second, we
iteratively select high-confidence data from the low-confidence data pool by
thresholding the softmax probabilities, prototype labels, and intra-class
similarity. Then, we iteratively train a stronger target network based on the
crude target model to correct the wrongly labeled samples to improve the
accuracy of the pseudo-label. Experimental results demonstrate that the
proposed method achieves state-of-the-art black-box unsupervised domain
adaptation performance on three benchmark datasets.

通过增量选择高置信度伪标签来改善目标模型的泛化能力，实验结果表明该方法在三个基准数据集上达到了最先进的黑盒无监督领域适应性能。

黑盒无监督领域自适应的增量伪标签

Incremental Pseudo-Labeling for Black-Box Unsupervised Domain Adaptation

Black-box unsupervised domain adaptation (UDA) learns with source predictions
of target data without accessing either source data or source models during
training, and it has clear superiority in data privacy and flexibility in
target network selection. However, the source predictions of target data are
often noisy and training with them is prone to learning collapses. We propose
BiMem, a bi-directional memorization mechanism that learns to remember useful
and representative information to correct noisy pseudo labels on the fly,
leading to robust black-box UDA that can generalize across different visual
recognition tasks. BiMem constructs three types of memory, including sensory
memory, short-term memory, and long-term memory, which interact in a
bi-directional manner for comprehensive and robust memorization of learnt
features. It includes a forward memorization flow that identifies and stores
useful features and a backward calibration flow that rectifies features' pseudo
labels progressively. Extensive experiments show that BiMem achieves superior
domain adaptation performance consistently across various visual recognition
tasks such as image classification, semantic segmentation and object detection.

使用黑盒子无监督领域自适应（UDA）的源预测目标数据而不访问源数据或源模型，并且在数据隐私和目标网络选择方面具有明显的优势，然而目标数据的源预测通常存在噪声，使用它们进行训练容易出现学习崩溃，我们提出了 BiMem，一种双向记忆机制，可以学会动态地纠正有噪声的伪标签，从而实现跨不同视觉识别任务的稳健的黑盒子 UDA，BiMem 构建了三种类型的记忆，包括感官记忆、短期记忆和长期记忆，它们以双向方式相互作用，全面而稳健地记忆所学特征，该方法在图像分类、语义分割和目标检测等各种视觉识别任务中持续实现了卓越的领域自适应性能。

黑盒无监督领域适应与双向阿特金森 - 希夫林记忆

Black-box Unsupervised Domain Adaptation with Bi-directional  Atkinson-Shiffrin Memory

Unsupervised domain adaptation (UDA) aims to learn models for a target domain
of unlabeled data by transferring knowledge from a labeled source domain. In
the traditional UDA setting, labeled source data are assumed to be available
for adaptation. Due to increasing concerns for data privacy, source-free UDA is
highly appreciated as a new UDA setting, where only a trained source model is
assumed to be available, while labeled source data remain private. However,
trained source models may also be unavailable in practice since source models
may have commercial values and exposing source models brings risks to the
source domain, e.g., problems of model misuse and white-box attacks. In this
work, we study a subtly different setting, named Black-Box Unsupervised Domain
Adaptation (B$^2$UDA), where only the application programming interface of
source model is accessible to the target domain; in other words, the source
model itself is kept as a black-box one. To tackle B$^2$UDA, we propose a
simple yet effective method, termed Iterative Learning with Noisy Labels
(IterLNL). With black-box models as tools of noisy labeling, IterLNL conducts
noisy labeling and learning with noisy labels (LNL), iteratively. To facilitate
the implementation of LNL in B$^2$UDA, we estimate the noise rate from model
predictions of unlabeled target data and propose category-wise sampling to
tackle the unbalanced label noise among categories. Experiments on benchmark
datasets show the efficacy of IterLNL. Given neither source data nor source
models, IterLNL performs comparably with traditional UDA methods that make full
use of labeled source data.

本篇论文研究了源领域无标签数据的黑盒非监督域适应 (B$^2$UDA) 问题，在此基础上提出了一种名为 Iterative Learning with Noisy Labels (IterLNL) 的简单有效方法，通过黑盒模型作为噪声标签工具进行噪声标注和学习，迭代解决该问题。实验表明，无需源数据和源模型，IterLNL 的表现可与传统有标签源数据的 UDA 方法相媲美。