Exploring and modeling rain generation mechanism is critical for augmenting
paired data to ease training of rainy image processing models. Against this
task, this study proposes a novel deep learning based rain generator, which
fully takes the physical generation mechanism underlying rains into
consideration and well encodes the learning of the fundamental rain factors
(i.e., shape, orientation, length, width and sparsity) explicitly into the deep
network. Its significance lies in that the generator not only elaborately
design essential elements of the rain to simulate expected rains, like
conventional artificial strategies, but also finely adapt to complicated and
diverse practical rainy images, like deep learning methods. By rationally
adopting filter parameterization technique, we first time achieve a deep
network that is finely controllable with respect to rain factors and able to
learn the distribution of these factors purely from data. Our unpaired
generation experiments demonstrate that the rain generated by the proposed rain
generator is not only of higher quality, but also more effective for deraining
and downstream tasks compared to current state-of-the-art rain generation
methods. Besides, the paired data augmentation experiments, including both
in-distribution and out-of-distribution (OOD), further validate the diversity
of samples generated by our model for in-distribution deraining and OOD
generalization tasks.

探索和建模降雨生成机制对于增加配对数据减轻降雨图像处理模型的训练任务至关重要，该研究提出了一种新颖的基于深度学习的降雨生成器，充分考虑了降雨背后的物理生成机制，并将基本降雨因素（即形状、方向、长度、宽度和稀疏度）的学习明确地编码到深度网络中。我们首次通过合理采用滤波器参数化技术实现了一个在降雨因素上可精细控制且能够纯粹通过数据学习这些因素的深度网络。我们的生成实验表明，由提出的降雨生成器生成的降雨不仅质量更高，而且对于去雨和下游任务比目前最先进的降雨生成方法更有效。此外，我们的配对数据增强实验，包括分布内和分布外（OOD），进一步验证了我们模型生成的样本的多样性，用于分布内去雨和 OOD 泛化任务。

TRG-Net：一种可解释和可控的雨生成器

TRG-Net: An Interpretable and Controllable Rain Generator

Large models, encompassing large language and diffusion models, have shown
exceptional promise in approximating human-level intelligence, garnering
significant interest from both academic and industrial spheres. However, the
training of these large models necessitates vast quantities of high-quality
data, and with continuous updates to these models, the existing reservoir of
high-quality data may soon be depleted. This challenge has catalyzed a surge in
research focused on data augmentation methods. Leveraging large models, these
data augmentation techniques have outperformed traditional approaches. This
paper offers an exhaustive review of large model-driven data augmentation
methods, adopting a comprehensive perspective. We begin by establishing a
classification of relevant studies into three main categories: image
augmentation, text augmentation, and paired data augmentation. Following this,
we delve into various data post-processing techniques pertinent to large
model-based data augmentation. Our discussion then expands to encompass the
array of applications for these data augmentation methods within natural
language processing, computer vision, and audio signal processing. We proceed
to evaluate the successes and limitations of large model-based data
augmentation across different scenarios. Concluding our review, we highlight
prospective challenges and avenues for future exploration in the field of data
augmentation. Our objective is to furnish researchers with critical insights,
ultimately contributing to the advancement of more sophisticated large models.
We consistently maintain the related open-source materials at:
this https URL

大型模型驱动的数据增强方法的综述，包括图像增强、文本增强和配对数据增强三个主要类别以及相关的数据后处理技术和应用领域，评估了大型模型驱动的数据增强在不同场景下的成功和限制，并提出了未来研究的挑战和方向。