A multitude of industries depend on accurate and reasonable tabular data
augmentation for their business processes. Contemporary methodologies in
generating tabular data revolve around utilizing Generative Adversarial
Networks (GAN) or fine-tuning Large Language Models (LLM). However, GAN-based
approaches are documented to produce samples with common-sense errors
attributed to the absence of external knowledge. On the other hand, LLM-based
methods exhibit a limited capacity to capture the disparities between
synthesized and actual data distribution due to the absence of feedback from a
discriminator during training. Furthermore, the decoding of LLM-based
generation introduces gradient breakpoints, impeding the backpropagation of
loss from a discriminator, thereby complicating the integration of these two
approaches. To solve this challenge, we propose using proximal policy
optimization (PPO) to apply GANs, guiding LLMs to enhance the probability
distribution of tabular features. This approach enables the utilization of LLMs
as generators for GANs in synthesizing tabular data. Our experiments
demonstrate that PPO leads to an approximately 4\% improvement in the accuracy
of models trained on synthetically generated data over state-of-the-art across
three real-world datasets.

利用近似策略优化（PPO）引导大语言模型（LLM）来增强表格特征的概率分布，从而使 LLM 成为用于合成表格数据的生成器，在三个真实数据集上通过实验证明，该方法相对于最先进技术提高了约 4％的合成模型准确性。

P-TA：使用近端策略优化增强大型语言模型下的表格数据增强技术

P-TA: Using Proximal Policy Optimization to Enhance Tabular Data  Augmentation via Large Language Models

The availability of training data is one of the main limitations in deep
learning applications for medical imaging. Data augmentation is a popular
approach to overcome this problem. A new approach is a Machine Learning based
augmentation, in particular usage of Generative Adversarial Networks (GAN). In
this case, GANs generate images similar to the original dataset so that the
overall training data amount is bigger, which leads to better performance of
trained networks. A GAN model consists of two networks, a generator and a
discriminator interconnected in a feedback loop which creates a competitive
environment. This work is a continuation of the previous research where we
trained StyleGAN2-ADA by Nvidia on the limited COVID-19 chest X-ray image
dataset. In this paper, we study the dependence of the GAN-based augmentation
performance on dataset size with a focus on small samples. Two datasets are
considered, one with 1000 images per class (4000 images in total) and the
second with 500 images per class (2000 images in total). We train StyleGAN2-ADA
with both sets and then, after validating the quality of generated images, we
use trained GANs as one of the augmentations approaches in multi-class
classification problems. We compare the quality of the GAN-based augmentation
approach to two different approaches (classical augmentation and no
augmentation at all) by employing transfer learning-based classification of
COVID-19 chest X-ray images. The results are quantified using different
classification quality metrics and compared to the results from the literature.
The GAN-based augmentation approach is found to be comparable with classical
augmentation in the case of medium and large datasets but underperforms in the
case of smaller datasets. The correlation between the size of the original
dataset and the quality of classification is visible independently from the
augmentation approach.

基于 GANS 的数据增强方法在中等和大型数据集的情况下与传统增强方法相当，但在较小的数据集的情况下表现不佳。无论采用何种增强方法，原始数据集的大小与分类质量之间存在相关性。

GAN-based 增强深度学习 COVID-19 图像分类的进一步探索

Additional Look into GAN-based Augmentation for Deep Learning COVID-19  Image Classification

Semantic inpainting or image completion alludes to the task of inferring
arbitrary large missing regions in images based on image semantics. Since the
prediction of image pixels requires an indication of high-level context, this
makes it significantly tougher than image completion, which is often more
concerned with correcting data corruption and removing entire objects from the
input image. On the other hand, image enhancement attempts to eliminate
unwanted noise and blur from the image, along with sustaining most of the image
details. Efficient image completion and enhancement model should be able to
recover the corrupted and masked regions in images and then refine the image
further to increase the quality of the output image. Generative Adversarial
Networks (GAN), have turned out to be helpful in picture completion tasks. In
this chapter, we will discuss the underlying GAN architecture and how they can
be used used for image completion tasks.

语义修复，图像完成，图像增强，生成对抗网络 (GAN)，图像质量。

使用生成对抗网络的语义图像补全与增强

Semantic Image Completion and Enhancement using GANs

Inspired by classic generative adversarial networks (GAN), we propose a novel
end-to-end adversarial neural network, called SegAN, for the task of medical
image segmentation. Since image segmentation requires dense, pixel-level
labeling, the single scalar real/fake output of a classic GAN's discriminator
may be ineffective in producing stable and sufficient gradient feedback to the
networks. Instead, we use a fully convolutional neural network as the segmentor
to generate segmentation label maps, and propose a novel adversarial critic
network with a multi-scale $L_1$ loss function to force the critic and
segmentor to learn both global and local features that capture long- and
short-range spatial relationships between pixels. In our SegAN framework, the
segmentor and critic networks are trained in an alternating fashion in a
min-max game: The critic takes as input a pair of images, (original_image $*$
predicted_label_map, original_image $*$ ground_truth_label_map), and then is
trained by maximizing a multi-scale loss function; The segmentor is trained
with only gradients passed along by the critic, with the aim to minimize the
multi-scale loss function. We show that such a SegAN framework is more
effective and stable for the segmentation task, and it leads to better
performance than the state-of-the-art U-net segmentation method. We tested our
SegAN method using datasets from the MICCAI BRATS brain tumor segmentation
challenge. Extensive experimental results demonstrate the effectiveness of the
proposed SegAN with multi-scale loss: on BRATS 2013 SegAN gives performance
comparable to the state-of-the-art for whole tumor and tumor core segmentation
while achieves better precision and sensitivity for Gd-enhance tumor core
segmentation; on BRATS 2015 SegAN achieves better performance than the
state-of-the-art in both dice score and precision.

本研究提出了一种名为 SegAN 的端到端对抗神经网络，用于医学图像分割任务，它通过采用全卷积神经网络作为段分割器、提出一种新的对抗批判者网络，并使用多尺度 L1 损失函数来强制批判者和分割器学习全局和局部特征，从而实现更稳定、更有效的分割。在使用来自 MICCAI BRATS 脑肿瘤分割挑战的数据集进行测试后，表明 SegAN 具有优异的性能。