Real-world Super-Resolution (real-SR) methods focus on dealing with diverse
real-world images and have attracted increasing attention in recent years. The
key idea is to use a complex and high-order degradation model to mimic
real-world degradations. Although they have achieved impressive results in
various scenarios, they are faced with the obstacle of evaluation. Currently,
these methods are only assessed by their average performance on a small set of
degradation cases randomly selected from a large space, which fails to provide
a comprehensive understanding of their overall performance and often yields
biased results. To overcome the limitation in evaluation, we propose SEAL, a
framework for systematic evaluation of real-SR. In particular, we cluster the
extensive degradation space to create a set of representative degradation
cases, which serves as a comprehensive test set. Next, we propose a
coarse-to-fine evaluation protocol to measure the distributed and relative
performance of real-SR methods on the test set. The protocol incorporates two
new metrics: acceptance rate (AR) and relative performance ratio (RPR), derived
from an acceptance line and an excellence line. Under SEAL, we benchmark
existing real-SR methods, obtain new observations and insights into their
performance, and develop a new strong baseline. We consider SEAL as the first
step towards creating an unbiased and comprehensive evaluation platform, which
can promote the development of real-SR.

系统性评估实际超分辨率方法的框架 SEAL，利用代表性降级案例集群和新的评估指标衡量其性能，展示对现有方法的基准测试、新观察和新基线开发。

SEAL：一种用于对实际世界超分辨率系统进行系统评估的框架

SEAL: A Framework for Systematic Evaluation of Real-World  Super-Resolution

Label hierarchy is an important source of external knowledge that can enhance
classification performance. However, most existing methods rely on predefined
label hierarchies that may not match the data distribution. To address this
issue, we propose Simultaneous label hierarchy Exploration And Learning (SEAL),
a new framework that explores the label hierarchy by augmenting the observed
labels with latent labels that follow a prior hierarchical structure. Our
approach uses a 1-Wasserstein metric over the tree metric space as an objective
function, which enables us to simultaneously learn a data-driven label
hierarchy and perform (semi-)supervised learning. We evaluate our method on
several datasets and show that it achieves superior results in both supervised
and semi-supervised scenarios and reveals insightful label structures. Our
implementation is available at this https URL

提出了一种名为 SEAL 的框架，该框架探索了标签层次结构，通过扩充观察到的标签与先前的分层结构的潜在标签进行了学习，并使用 1-Wasserstein 度量来同时执行数据驱动标签分层和（半）监督学习， 在几个数据集上进行了验证，结果表明该方法在监督和半监督场景下都取得了优异的成果，并揭示了有启发性的标签结构。

SEAL: 同步标签层次探索和学习

SEAL: Simultaneous Label Hierarchy Exploration And Learning

A vast amount of valuable data is produced and is becoming available for
analysis as a result of advancements in smart cyber-physical systems. The data
comes from various sources, such as healthcare, smart homes, smart vehicles,
and often includes private, potentially sensitive information that needs
appropriate sanitization before being released for analysis. The incremental
and fast nature of data generation in these systems necessitates scalable
privacy-preserving mechanisms with high privacy and utility. However, privacy
preservation often comes at the expense of data utility. We propose a new data
perturbation algorithm, SEAL (Secure and Efficient data perturbation Algorithm
utilizing Local differential privacy), based on Chebyshev interpolation and
Laplacian noise, which provides a good balance between privacy and utility with
high efficiency and scalability. Empirical comparisons with existing
privacy-preserving algorithms show that SEAL excels in execution speed,
scalability, accuracy, and attack resistance. SEAL provides flexibility in
choosing the best possible privacy parameters, such as the amount of added
noise, which can be tailored to the domain and dataset.

提出了一种新的数据扰动算法 SEAL，它基于 Chebyshev 插值和 Laplacian 噪声，在高效性和可扩展性方面提供了高隐私、高数据效用的平衡，在隐私参数选择方面提供了灵活性，并显示出优秀的执行速度、可扩展性、准确性和攻击抵抗力。