With recent advances in computing hardware and surges of deep-learning
architectures, learning-based deep image registration methods have surpassed
their traditional counterparts, in terms of metric performance and inference
time. However, these methods focus on improving performance measurements such
as Dice, resulting in less attention given to model behaviors that are equally
desirable for registrations, especially for medical imaging. This paper
investigates these behaviors for popular learning-based deep registrations
under a sanity-checking microscope. We find that most existing registrations
suffer from low inverse consistency and nondiscrimination of identical pairs
due to overly optimized image similarities. To rectify these behaviors, we
propose a novel regularization-based sanity-enforcer method that imposes two
sanity checks on the deep model to reduce its inverse consistency errors and
increase its discriminative power simultaneously. Moreover, we derive a set of
theoretical guarantees for our sanity-checked image registration method, with
experimental results supporting our theoretical findings and their
effectiveness in increasing the sanity of models without sacrificing any
performance. Our code and models are available at
https://github.com/tuffr5/Saner-deep-registration.

本研究论文调查了流行的基于深度学习的图像配准方法的模型行为，并提出了一种基于正规化的新颖的合理性验证方法来减少逆一致性错误并同时增加鉴别能力。通过实验结果支持理论发现，证明了该方法在提高模型合理性方面的有效性。

迈向更合理的深度图像配准

Towards Saner Deep Image Registration

As one of the most successful AI-powered applications, recommender systems
aim to help people make appropriate decisions in an effective and efficient
way, by providing personalized suggestions in many aspects of our lives,
especially for various human-oriented online services such as e-commerce
platforms and social media sites. In the past few decades, the rapid
developments of recommender systems have significantly benefited human by
creating economic value, saving time and effort, and promoting social good.
However, recent studies have found that data-driven recommender systems can
pose serious threats to users and society, such as spreading fake news to
manipulate public opinion in social media sites, amplifying unfairness toward
under-represented groups or individuals in job matching services, or inferring
privacy information from recommendation results. Therefore, systems'
trustworthiness has been attracting increasing attention from various aspects
for mitigating negative impacts caused by recommender systems, so as to enhance
the public's trust towards recommender systems techniques. In this survey, we
provide a comprehensive overview of Trustworthy Recommender systems (TRec) with
a specific focus on six of the most important aspects; namely, Safety &
Robustness, Nondiscrimination & Fairness, Explainability, Privacy,
Environmental Well-being, and Accountability & Auditability. For each aspect,
we summarize the recent related technologies and discuss potential research
directions to help achieve trustworthy recommender systems in the future.

该研究论文概述了可信度推荐系统的六个最重要方面：安全性与鲁棒性、非歧视与公平性、可解释性、隐私性、环境福祉以及问责与审计，并讨论了未来实现可信度推荐系统的潜在研究方向。

可信推荐系统综述

A Comprehensive Survey on Trustworthy Recommender Systems

We mathematically compare four competing definitions of group-level
nondiscrimination: demographic parity, equalized odds, predictive parity, and
calibration. Using the theoretical framework of Friedler et al., we study the
properties of each definition under various worldviews, which are assumptions
about how, if at all, the observed data is biased. We argue that different
worldviews call for different definitions of fairness, and we specify the
worldviews that, when combined with the desire to avoid a criterion for
discrimination that we call disparity amplification, motivate demographic
parity and equalized odds. We also argue that predictive parity and calibration
are insufficient for avoiding disparity amplification because predictive parity
allows an arbitrarily large inter-group disparity and calibration is not robust
to post-processing. Finally, we define a worldview that is more realistic than
the previously considered ones, and we introduce a new notion of fairness that
corresponds to this worldview.

通过对四种竞争性的群体非歧视定义：人口平等、等候平等、预测平等、校准；的数学比较，我们研究了不同世界观下，每个定义的属性，指出了不同的世界观需要不同的公平定义，并且界定了一个与更真实的世界观对应的新的公平概念。