Reference-based image super-resolution (RefSR) has shown promising success in
recovering high-frequency details by utilizing an external reference image
(Ref). In this task, texture details are transferred from the Ref image to the
low-resolution (LR) image according to their point- or patch-wise
correspondence. Therefore, high-quality correspondence matching is critical. It
is also desired to be computationally efficient. Besides, existing RefSR
methods tend to ignore the potential large disparity in distributions between
the LR and Ref images, which hurts the effectiveness of the information
utilization. In this paper, we propose the MASA network for RefSR, where two
novel modules are designed to address these problems. The proposed Match &
Extraction Module significantly reduces the computational cost by a
coarse-to-fine correspondence matching scheme. The Spatial Adaptation Module
learns the difference of distribution between the LR and Ref images, and remaps
the distribution of Ref features to that of LR features in a spatially adaptive
way. This scheme makes the network robust to handle different reference images.
Extensive quantitative and qualitative experiments validate the effectiveness
of our proposed model.

本文提出 MASA 网络用于参考图像超分辨率 (RefSR)，其中提出了两个创新模块用于解决当前方法中存在的计算开销大、分布不一致等问题，并通过定量和定性实验验证了模型的有效性。

MASA-SR: 基于参考图像的图像超分辨率匹配加速与空间自适应

MASA-SR: Matching Acceleration and Spatial Adaptation for  Reference-Based Image Super-Resolution

Classical machine learning assumes that the training and test sets come from
the same distributions. Therefore, a model learned from the labeled training
data is expected to perform well on the test data. However, This assumption may
not always hold in real-world applications where the training and the test data
fall from different distributions, due to many factors, e.g., collecting the
training and test sets from different sources, or having an out-dated training
set due to the change of data over time. In this case, there would be a
discrepancy across domain distributions, and naively applying the trained model
on the new dataset may cause degradation in the performance. Domain adaptation
is a sub-field within machine learning that aims to cope with these types of
problems by aligning the disparity between domains such that the trained model
can be generalized into the domain of interest. This paper focuses on
unsupervised domain adaptation, where the labels are only available in the
source domain. It addresses the categorization of domain adaptation from
different viewpoints. Besides, It presents some successful shallow and deep
domain adaptation approaches that aim to deal with domain adaptation problems.

本文关注的是无监督领域适应问题，该问题中只在源域内提供标签。文章试图从不同角度解释领域适应的类别，并提出了一些成功的浅层和深层领域适应方法。