The promise of tabular generative models is to produce realistic synthetic
data that can be shared and safely used without dangerous leakage of
information from the training set. In evaluating these models, a variety of
methods have been proposed to measure the tendency to copy data from the
training dataset when generating a sample. However, these methods suffer from
either not considering data-copying from a privacy threat perspective, not
being motivated by recent results in the data-copying literature or being
difficult to make compatible with the high dimensional, mixed type nature of
tabular data. This paper proposes a new similarity metric and Membership
Inference Attack called Data Plagiarism Index (DPI) for tabular data. We show
that DPI evaluates a new intuitive definition of data-copying and characterizes
the corresponding privacy risk. We show that the data-copying identified by DPI
poses both privacy and fairness threats to common, high performing
architectures; underscoring the necessity for more sophisticated generative
modeling techniques to mitigate this issue.

该论文提出了一种新的相似性度量和数据剽窃指数（DPI）来评估表格数据的数据复制，并表征了相应的隐私风险。同时，论文指出 DPI 鉴定的数据复制对常见的高性能架构而言存在隐私和公平威胁，强调了需要更复杂的生成模型技术来减轻这个问题。

数据剽窃指数：表格生成模型中的数据复制隐私风险特征

Data Plagiarism Index: Characterizing the Privacy Risk of Data-Copying  in Tabular Generative Models

Identification of tumor margins is essential for surgical decision-making for
glioblastoma patients and provides reliable assistance for neurosurgeons.
Despite improvements in deep learning architectures for tumor segmentation over
the years, creating a fully autonomous system suitable for clinical floors
remains a formidable challenge because the model predictions have not yet
reached the desired level of accuracy and generalizability for clinical
applications. Generative modeling techniques have seen significant improvements
in recent times. Specifically, Generative Adversarial Networks (GANs) and
Denoising-diffusion-based models (DDPMs) have been used to generate
higher-quality images with fewer artifacts and finer attributes. In this work,
we introduce a framework called Re-Diffinet for modeling the discrepancy
between the outputs of a segmentation model like U-Net and the ground truth,
using DDPMs. By explicitly modeling the discrepancy, the results show an
average improvement of 0.55\% in the Dice score and 16.28\% in HD95 from
cross-validation over 5-folds, compared to the state-of-the-art U-Net
segmentation model.

通过使用 DDPM 模型，我们介绍了一种名为 Re-Diffinet 的框架，用于建模分割模型与真实情况之间的差异，从而提高肿瘤分割的 Dice 评分 0.55% 和 HD95 16.28% 的准确性。

Re-DiffiNet：利用扩散建模肿瘤分割中的差异

Re-DiffiNet: Modeling discrepancies in tumor segmentation using  diffusion

We introduce Primal-Dual Wasserstein GAN, a new learning algorithm for
building latent variable models of the data distribution based on the primal
and the dual formulations of the optimal transport (OT) problem. We utilize the
primal formulation to learn a flexible inference mechanism and to create an
optimal approximate coupling between the data distribution and the generative
model. In order to learn the generative model, we use the dual formulation and
train the decoder adversarially through a critic network that is regularized by
the approximate coupling obtained from the primal. Unlike previous methods that
violate various properties of the optimal critic, we regularize the norm and
the direction of the gradients of the critic function. Our model shares many of
the desirable properties of auto-encoding models in terms of mode coverage and
latent structure, while avoiding their undesirable averaging properties, e.g.
their inability to capture sharp visual features when modeling real images. We
compare our algorithm with several other generative modeling techniques that
utilize Wasserstein distances on Frechet Inception Distance (FID) and Inception
Scores (IS).

我们引入了 Primal-Dual Wasserstein GAN 作为一种新的学习算法，用于基于最优输运问题的原始和对偶公式建立数据分布的潜在变量模型，同时通过对偶公式训练解码器实现生成模型，通过适当的正则化来保留最优批评者的性质，实现比以前的方法更好的图像生成效果。