The data bottleneck has emerged as a fundamental challenge in learning based
image restoration methods. Researchers have attempted to generate synthesized
training data using paired or unpaired samples to address this challenge. This
study proposes SeNM-VAE, a semi-supervised noise modeling method that leverages
both paired and unpaired datasets to generate realistic degraded data. Our
approach is based on modeling the conditional distribution of degraded and
clean images with a specially designed graphical model. Under the variational
inference framework, we develop an objective function for handling both paired
and unpaired data. We employ our method to generate paired training samples for
real-world image denoising and super-resolution tasks. Our approach excels in
the quality of synthetic degraded images compared to other unpaired and paired
noise modeling methods. Furthermore, our approach demonstrates remarkable
performance in downstream image restoration tasks, even with limited paired
data. With more paired data, our method achieves the best performance on the
SIDD dataset.

该研究提出了 SeNM-VAE，一种半监督噪声建模方法，利用配对和非配对数据集生成逼真的退化数据。通过特殊设计的图形模型对退化和清晰图像的条件分布进行建模，在变分推断框架下开发了处理配对和非配对数据的客观函数。我们将该方法应用于真实世界的图像去噪和超分辨率任务中，其合成的退化图像质量优于其他非配对和配对噪声建模方法。此外，即使在有限的配对数据的情况下，我们的方法在下游图像恢复任务中也表现出色。随着更多的配对数据，我们的方法在 SIDD 数据集上实现了最佳性能。

SeNM-VAE：具有层级变分自编码器的半监督噪声建模

SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational  Autoencoder

Machine learning (ML) is revolutionizing the world, affecting almost every
field of science and industry. Recent algorithms (in particular, deep networks)
are increasingly data-hungry, requiring large datasets for training. Thus, the
dominant paradigm in ML today involves constructing large, task-specific
datasets.
However, obtaining quality datasets of such magnitude proves to be a
difficult challenge. A variety of methods have been proposed to address this
data bottleneck problem, but they are scattered across different areas, and it
is hard for a practitioner to keep up with the latest developments. In this
work, we propose a taxonomy of these methods. Our goal is twofold: (1) We wish
to raise the community's awareness of the methods that already exist and
encourage more efficient use of resources, and (2) we hope that such a taxonomy
will contribute to our understanding of the problem, inspiring novel ideas and
strategies to replace current annotation-heavy approaches.

介绍了如何建立大规模数据集用于机器学习，提出了处理数据瓶颈问题的分类方法，旨在提高社区对这些方法的认识并促进资源的更有效利用。