Image-level regression is an important task in Earth observation, where
visual domain and label shifts are a core challenge hampering generalization.
However, cross-domain regression with remote sensing data remains understudied
due to the absence of suited datasets. We introduce a new dataset with aerial
and satellite imagery in five countries with three forest-related regression
tasks. To match real-world applicative interests, we compare methods through a
restrictive setup where no prior on the target domain is available during
training, and models are adapted with limited information during testing.
Building on the assumption that ordered relationships generalize better, we
propose manifold diffusion for regression as a strong baseline for transduction
in low-data regimes. Our comparison highlights the comparative advantages of
inductive and transductive methods in cross-domain regression.

通过限制性设置，我们在没有目标域的先验的情况下进行方法比较，以及测试阶段的有限信息适应性调整，研究了交叉域回归中彼此对比优势的归纳和传导方法。

整理嵌入空间：用于森林监测的领域自适应回归

Get Your Embedding Space in Order: Domain-Adaptive Regression for Forest  Monitoring

Domain adaptation (DA) aims to transfer the knowledge of a well-labeled
source domain to facilitate unlabeled target learning. When turning to specific
tasks such as indoor (Wi-Fi) localization, it is essential to learn a
cross-domain regressor to mitigate the domain shift. This paper proposes a
novel method Adversarial Bi-Regressor Network (ABRNet) to seek more effective
cross-domain regression model. Specifically, a discrepant bi-regressor
architecture is developed to maximize the difference of bi-regressor to
discover uncertain target instances far from the source distribution, and then
an adversarial training mechanism is adopted between feature extractor and dual
regressors to produce domain-invariant representations. To further bridge the
large domain gap, a domain-specific augmentation module is designed to
synthesize two source-similar and target-similar intermediate domains to
gradually eliminate the original domain mismatch. The empirical studies on two
cross-domain regressive benchmarks illustrate the power of our method on
solving the domain adaptive regression (DAR) problem.

本文提出了一种名为 Adversarial Bi-Regressor Network (ABRNet) 的方法，旨在通过采用对抗训练机制和领域特定的数据增强模块，帮助解决领域自适应回归（DAR）问题，实现在室内（Wi-Fi）定位等特定任务中学习跨领域回归模型的目的。