Domain generalization aims to learn a generalization model that can perform
well on unseen test domains by only training on limited source domains.
However, existing domain generalization approaches often bring in
prediction-irrelevant noise or require the collection of domain labels. To
address these challenges, we consider the domain generalization problem from a
different perspective by categorizing underlying feature groups into
domain-shared and domain-specific features. Nevertheless, the domain-specific
features are difficult to be identified and distinguished from the input data.
In this work, we propose DomaIn-SPEcific Liberating (DISPEL), a post-processing
fine-grained masking approach that can filter out undefined and
indistinguishable domain-specific features in the embedding space.
Specifically, DISPEL utilizes a mask generator that produces a unique mask for
each input data to filter domain-specific features. The DISPEL framework is
highly flexible to be applied to any fine-tuned models. We derive a
generalization error bound to guarantee the generalization performance by
optimizing a designed objective loss. The experimental results on five
benchmarks demonstrate DISPEL outperforms existing methods and can further
generalize various algorithms.

本文从识别不同的属性组作为领域共享或领域特有属性，提出了一个称为 DISPEL 的后处理微调方法，该方法可以过滤掉嵌入空间中未定义和无法区分的领域特有特征。DISPEL 框架高度灵活，可以应用于任何微调模型，实验结果在五个基准测试上表明 DISPEL 优于现有方法，并且可以进一步推广各种算法。

DISPEL：领域通用性通过领域特定的解放

DISPEL: Domain Generalization via Domain-Specific Liberating

While great progress has been made recently in automatic image manipulation,
it has been limited to object centric images like faces or structured scene
datasets. In this work, we take a step towards general scene-level image
editing by developing an automatic interaction-free object removal model. Our
model learns to find and remove objects from general scene images using
image-level labels and unpaired data in a generative adversarial network (GAN)
framework. We achieve this with two key contributions: a two-stage editor
architecture consisting of a mask generator and image in-painter that
co-operate to remove objects, and a novel GAN based prior for the mask
generator that allows us to flexibly incorporate knowledge about object shapes.
We experimentally show on two datasets that our method effectively removes a
wide variety of objects using weak supervision only

本文旨在研发一种自动化、不需要交互即可实现目标物体移除的模型，其基于生成对抗网络框架，利用图像级别标签和非配对数据进行训练。通过两个关键贡献：一个由掩膜生成器和图像修复器组成的两阶段编辑器体系结构，以及一种基于 GAN 的先验模型，该模型能灵活地整合物体形状知识。我们在两个数据集上进行实验表明，该方法能够有效地去除各种不同的物体，仅需要较弱的监督学习。