Generative adversarial networks (GANs) have drawn considerable attention in
recent years for their proven capability in generating synthetic data which can
be utilized for multiple purposes. While GANs have demonstrated tremendous
successes in producing synthetic data samples that replicate the dynamics of
the original datasets, the validity of the synthetic data and the underlying
privacy concerns represent major challenges which are not sufficiently
addressed. In this work, we design a cascaded tabular GAN framework (CasTGAN)
for generating realistic tabular data with a specific focus on the validity of
the output. In this context, validity refers to the the dependency between
features that can be found in the real data, but is typically misrepresented by
traditional generative models. Our key idea entails that employing a cascaded
architecture in which a dedicated generator samples each feature, the synthetic
output becomes more representative of the real data. Our experimental results
demonstrate that our model well captures the constraints and the correlations
between the features of the real data, especially the high dimensional
datasets. Furthermore, we evaluate the risk of white-box privacy attacks on our
model and subsequently show that applying some perturbations to the auxiliary
learners in CasTGAN increases the overall robustness of our model against
targeted attacks.

本文提出了一个级联表格生成对抗网络框架（CasTGAN），可生成更逼真的表格数据，模型能捕捉实际数据的限制和相关性，并通过对辅助学习者进行扰动来提高模型抵抗特定攻击的整体稳健性。

CasTGAN: 级联生成对抗网络用于逼真的表格数据合成

CasTGAN: Cascaded Generative Adversarial Network for Realistic Tabular  Data Synthesis

Scene graph generation (SGG) is to detect object pairs with their relations
in an image. Existing SGG approaches often use multi-stage pipelines to
decompose this task into object detection, relation graph construction, and
dense or dense-to-sparse relation prediction. Instead, from a perspective on
SGG as a direct set prediction, this paper presents a simple, sparse, and
unified framework, termed as Structured Sparse R-CNN. The key to our method is
a set of learnable triplet queries and a structured triplet detector which
could be jointly optimized from the training set in an end-to-end manner.
Specifically, the triplet queries encode the general prior for object pairs
with their relations, and provide an initial guess of scene graphs for
subsequent refinement. The triplet detector presents a cascaded architecture to
progressively refine the detected scene graphs with the customized dynamic
heads. In addition, to relieve the training difficulty of our method, we
propose a relaxed and enhanced training strategy based on knowledge
distillation from a Siamese Sparse R-CNN. We perform experiments on several
datasets: Visual Genome and Open Images V4/V6, and the results demonstrate that
our method achieves the state-of-the-art performance. In addition, we also
perform in-depth ablation studies to provide insights on our structured
modeling in triplet detector design and training strategies. The code and
models are made available at this https URL

本文提出一种名为 结构化稀疏 R-CNN 的框架，该框架从场景图生成（SGG）的角度出发，将其视为直接集合预测。文中使用可学习的三元组查询和结构化三元组检测器，并提出级联体系结构用于渐进地完善检测到的场景图。在多个数据集上进行的实验结果表明，该方法具有最先进的性能。