Most of the recent literature on image Super-Resolution (SR) can be
classified into two main approaches. The first one involves learning a
corruption model tailored to a specific dataset, aiming to mimic the noise and
corruption in low-resolution images, such as sensor noise. However, this
approach is data-specific, tends to lack adaptability, and its accuracy
diminishes when faced with unseen types of image corruptions. A second and more
recent approach, referred to as Robust Super-Resolution (RSR), proposes to
improve real-world SR by harnessing the generalization capabilities of a model
by making it robust to adversarial attacks. To delve further into this second
approach, our paper explores the universality of various methods for enhancing
the robustness of deep learning SR models. In other words, we inquire: "Which
robustness method exhibits the highest degree of adaptability when dealing with
a wide range of adversarial attacks ?". Our extensive experimentation on both
synthetic and real-world images empirically demonstrates that median randomized
smoothing (MRS) is more general in terms of robustness compared to adversarial
learning techniques, which tend to focus on specific types of attacks.
Furthermore, as expected, we also illustrate that the proposed universal robust
method enables the SR model to handle standard corruptions more effectively,
such as blur and Gaussian noise, and notably, corruptions naturally present in
real-world images. These results support the significance of shifting the
paradigm in the development of real-world SR methods towards RSR, especially
via MRS.

我们的研究探索了提高深度学习超分辨率模型鲁棒性的不同方法的普适性，并发现中值随机平滑在对抗攻击以及标准图像破坏方面比其他方法更具通用性。这些结果支持将真实世界超分辨率方法的发展重心转向鲁棒超分辨率。

基于中值随机平滑的通用稳健方法用于现实世界超分辨率

Universal Robustness via Median Randomized Smoothing for Real-World  Super-Resolution

The past decade has witnessed great strides in video recovery by specialist
technologies, like video inpainting, completion, and error concealment.
However, they typically simulate the missing content by manual-designed error
masks, thus failing to fill in the realistic video loss in video communication
(e.g., telepresence, live streaming, and internet video) and multimedia
forensics. To address this, we introduce the bitstream-corrupted video (BSCV)
benchmark, the first benchmark dataset with more than 28,000 video clips, which
can be used for bitstream-corrupted video recovery in the real world. The BSCV
is a collection of 1) a proposed three-parameter corruption model for video
bitstream, 2) a large-scale dataset containing rich error patterns, multiple
corruption levels, and flexible dataset branches, and 3) a plug-and-play module
in video recovery framework that serves as a benchmark. We evaluate
state-of-the-art video inpainting methods on the BSCV dataset, demonstrating
existing approaches' limitations and our framework's advantages in solving the
bitstream-corrupted video recovery problem. The benchmark and dataset are
released at this https URL

本研究介绍了首个具有超过 28,000 个视频剪辑的比特流损坏视频恢复的基准数据集 (BSCV)，它包含了三个参数的损坏模型、丰富的错误模式、多个损坏级别以及灵活的数据集分支，并利用 BSCV 数据集评估了最先进的视频修复方法，在比特流损坏视频恢复问题上展示了现有方法的局限性以及我们的框架的优势。

位流损坏的视频恢复：一个新的基准数据集和方法

Bitstream-Corrupted Video Recovery: A Novel Benchmark Dataset and Method

We present an efficient document representation learning framework, Document
Vector through Corruption (Doc2VecC). Doc2VecC represents each document as a
simple average of word embeddings. It ensures a representation generated as
such captures the semantic meanings of the document during learning. A
corruption model is included, which introduces a data-dependent regularization
that favors informative or rare words while forcing the embeddings of common
and non-discriminative ones to be close to zero. Doc2VecC produces
significantly better word embeddings than Word2Vec. We compare Doc2VecC with
several state-of-the-art document representation learning algorithms. The
simple model architecture introduced by Doc2VecC matches or out-performs the
state-of-the-art in generating high-quality document representations for
sentiment analysis, document classification as well as semantic relatedness
tasks. The simplicity of the model enables training on billions of words per
hour on a single machine. At the same time, the model is very efficient in
generating representations of unseen documents at test time.

本文介绍了一个有效的文档表示学习框架，Doc2VecC。Doc2VecC 通过词嵌入的简单平均来表示每个文档，并引入一个数据相关的正则化模型以捕捉文档的语义含义。该模型在训练时能够较好地捕捉文本信息，同时能够在生成文档表示方面与当前最先进算法匹配或超越，并且其模型结构简单，能够高效地处理海量文档。