X-ray images play a vital role in the intraoperative processes due to their
high resolution and fast imaging speed and greatly promote the subsequent
segmentation, registration and reconstruction. However, over-dosed X-rays
superimpose potential risks to human health to some extent. Data-driven
algorithms from volume scans to X-ray images are restricted by the scarcity of
paired X-ray and volume data. Existing methods are mainly realized by modelling
the whole X-ray imaging procedure. In this study, we propose a learning-based
approach termed CT2X-GAN to synthesize the X-ray images in an end-to-end manner
using the content and style disentanglement from three different image domains.
Our method decouples the anatomical structure information from CT scans and
style information from unpaired real X-ray images/ digital reconstructed
radiography (DRR) images via a series of decoupling encoders. Additionally, we
introduce a novel consistency regularization term to improve the stylistic
resemblance between synthesized X-ray images and real X-ray images. Meanwhile,
we also impose a supervised process by computing the similarity of computed
real DRR and synthesized DRR images. We further develop a pose attention module
to fully strengthen the comprehensive information in the decoupled content code
from CT scans, facilitating high-quality multi-view image synthesis in the
lower 2D space. Extensive experiments were conducted on the publicly available
CTSpine1K dataset and achieved 97.8350, 0.0842 and 3.0938 in terms of FID, KID
and defined user-scored X-ray similarity, respectively. In comparison with
3D-aware methods ($\pi$-GAN, EG3D), CT2X-GAN is superior in improving the
synthesis quality and realistic to the real X-ray images.

通过引入内容和风格的解耦技术以及姿态注意力模块，利用 CT2X-GAN 方法，该研究提出了一种学习式的端到端合成 X 射线图像的方法，从而改善合成质量并使其更接近真实的 X 射线图像。

CT 扫描中的多视角 X 射线图像合成与多域解缠

Multi-view X-ray Image Synthesis with Multiple Domain Disentanglement  from CT Scans

The duality of content and style is inherent to the nature of art. For
humans, these two elements are clearly different: content refers to the objects
and concepts in the piece of art, and style to the way it is expressed. This
duality poses an important challenge for computer vision. The visual appearance
of objects and concepts is modulated by the style that may reflect the author's
emotions, social trends, artistic movement, etc., and their deep comprehension
undoubtfully requires to handle both. A promising step towards a general
paradigm for art analysis is to disentangle content and style, whereas relying
on human annotations to cull a single aspect of artworks has limitations in
learning semantic concepts and the visual appearance of paintings. We thus
present GOYA, a method that distills the artistic knowledge captured in a
recent generative model to disentangle content and style. Experiments show that
synthetically generated images sufficiently serve as a proxy of the real
distribution of artworks, allowing GOYA to separately represent the two
elements of art while keeping more information than existing methods.

GOYA 是一种基于生成模型的绘画内容与风格分离的艺术分析方法，实验表明其可以更好地获取艺术作品的语义概念与视觉外观信息。

不仅是生成艺术：稳定扩散用于艺术分析中内容 - 风格解缠

Not Only Generative Art: Stable Diffusion for Content-Style  Disentanglement in Art Analysis

Machine learning models are vulnerable to Out-Of-Distribution (OOD) examples,
and such a problem has drawn much attention. However, current methods lack a
full understanding of different types of OOD data: there are benign OOD data
that can be properly adapted to enhance the learning performance, while other
malign OOD data would severely degenerate the classification result. To Harness
OOD data, this paper proposes a HOOD method that can leverage the content and
style from each image instance to identify benign and malign OOD data.
Particularly, we design a variational inference framework to causally
disentangle content and style features by constructing a structural causal
model. Subsequently, we augment the content and style through an intervention
process to produce malign and benign OOD data, respectively. The benign OOD
data contain novel styles but hold our interested contents, and they can be
leveraged to help train a style-invariant model. In contrast, the malign OOD
data inherit unknown contents but carry familiar styles, by detecting them can
improve model robustness against deceiving anomalies. Thanks to the proposed
novel disentanglement and data augmentation techniques, HOOD can effectively
deal with OOD examples in unknown and open environments, whose effectiveness is
empirically validated in three typical OOD applications including OOD
detection, open-set semi-supervised learning, and open-set domain adaptation.

本文提出了一种 HOOD 方法，通过设计一种基于因果推断的变分推理框架，利用每个图像实例的内容和风格来识别良性和恶性 ODD 数据，并通过干预过程增加内容和风格特征，从而产生良性和恶性 OOD 数据，HOOD 可以有效处理未知和开放环境中的 OOD 示例。

通过扩充内容和风格利用超出分布范围的例子

Harnessing Out-Of-Distribution Examples via Augmenting Content and Style

Eye-catching headlines function as the first device to trigger more clicks,
bringing reciprocal effect between producers and viewers. Producers can obtain
more traffic and profits, and readers can have access to outstanding articles.
When generating attractive headlines, it is important to not only capture the
attractive content but also follow an eye-catching written style. In this
paper, we propose a Disentanglement-based Attractive Headline Generator (DAHG)
that generates headline which captures the attractive content following the
attractive style. Concretely, we first devise a disentanglement module to
divide the style and content of an attractive prototype headline into latent
spaces, with two auxiliary constraints to ensure the two spaces are indeed
disentangled. The latent content information is then used to further polish the
document representation and help capture the salient part. Finally, the
generator takes the polished document as input to generate headline under the
guidance of the attractive style. Extensive experiments on the public Kuaibao
dataset show that DAHG achieves state-of-the-art performance. Human evaluation
also demonstrates that DAHG triggers 22% more clicks than existing models.

本文提出了基于解缠的吸引人标题生成器，通过内容和风格的解缠来生成符合吸引人风格的标题，实验表明 DAHG 在点击数方面表现出色，表现为最先进的水平。