Deep learning has revolutionized the field of artificial intelligence. Based
on the statistical correlations uncovered by deep learning-based methods,
computer vision technology has contributed to tremendous growth in areas such
as autonomous driving and robotics. Despite being the basis of deep learning,
such correlation is not stable and is susceptible to uncontrolled factors. In
the absence of the guidance of prior knowledge, statistical correlations can
easily turn into spurious correlations and cause confounders. As a result,
researchers are beginning to refine deep learning-based methods with causal
theory. Causal theory models the intrinsic causal structure unaffected by data
bias and is effective in avoiding spurious correlations. This paper aims to
comprehensively review the existing causal methods in typical vision and
vision-language tasks such as semantic segmentation, object detection, and
image captioning. The advantages of causality and the approaches for building
causal paradigms will be summarized. Future roadmaps are also proposed,
including facilitating the development of causal theory and its application in
other complex scenes and systems.

基于深度学习的方法已经颠覆了人工智能领域。本文综述了在典型的计算机视觉和视觉语言任务中，深度学习的基础、稳定性以及存在的问题，并总结了因果理论对解决这些问题的优势以及构建因果模型的方法。未来的规划包括促进因果理论的发展并在其他复杂场景和系统中应用。

典型计算机视觉任务中的因果推理

Causal reasoning in typical computer vision tasks

Data from populations of systems are prevalent in many industrial
applications. Machines and infrastructure are increasingly instrumented with
sensing systems, emitting streams of telemetry data with complex
interdependencies. In practice, data-centric monitoring procedures tend to
consider these assets (and respective models) as distinct -- operating in
isolation and associated with independent data. In contrast, this work captures
the statistical correlations and interdependencies between models of a group of
systems. Utilising a Bayesian multilevel approach, the value of data can be
extended, since the population can be considered as a whole, rather than
constituent parts. Most interestingly, domain expertise and knowledge of the
underlying physics can be encoded in the model at the system, subgroup, or
population level. We present an example of acoustic emission (time-of-arrival)
mapping for source location, to illustrate how multilevel models naturally lend
themselves to representing aggregate systems in engineering. In particular, we
focus on constraining the combined models with domain knowledge to enhance
transfer learning and enable further insights at the population level.

该文研究了一种基于 Bayesian 多层模型的方法，能够将各个系统之间的数据进行整合，并且能够将领域专业知识编码到系统、子组或整个有效群体的模型中，以强化转移学习并实现更深入的人口层面洞察。

将领域专业知识编码进多层模型以用于源位置确定

Encoding Domain Expertise into Multilevel Models for Source Location

Debiasing language models from unwanted behaviors in Natural Language
Understanding tasks is a topic with rapidly increasing interest in the NLP
community. Spurious statistical correlations in the data allow models to
perform shortcuts and avoid uncovering more advanced and desirable linguistic
features. A multitude of effective debiasing approaches has been proposed, but
flexibility remains a major issue. For the most part, models must be retrained
to find a new set of weights with debiased behavior. We propose a new debiasing
method in which we identify debiased pruning masks that can be applied to a
finetuned model. This enables the selective and conditional application of
debiasing behaviors. We assume that bias is caused by a certain subset of
weights in the network; our method is, in essence, a mask search to identify
and remove biased weights. Our masks show equivalent or superior performance to
the standard counterparts, while offering important benefits. Pruning masks can
be stored with high efficiency in memory, and it becomes possible to switch
among several debiasing behaviors (or revert back to the original biased model)
at inference time. Finally, it opens the doors to further research on how
biases are acquired by studying the generated masks. For example, we observed
that the early layers and attention heads were pruned more aggressively,
possibly hinting towards the location in which biases may be encoded.

介绍一种新的语言模型去偏置化的方法，使用剪枝口罩来确定模型去偏置化行为，不需要重新训练模型，可以高效地存储口罩并在推断时切换到多种去偏置行为。