The way the media presents events can significantly affect public perception,
which in turn can alter people's beliefs and views. Media bias describes a
one-sided or polarizing perspective on a topic. This article summarizes the
research on computational methods to detect media bias by systematically
reviewing 3140 research papers published between 2019 and 2022. To structure
our review and support a mutual understanding of bias across research domains,
we introduce the Media Bias Taxonomy, which provides a coherent overview of the
current state of research on media bias from different perspectives. We show
that media bias detection is a highly active research field, in which
transformer-based classification approaches have led to significant
improvements in recent years. These improvements include higher classification
accuracy and the ability to detect more fine-granular types of bias. However,
we have identified a lack of interdisciplinarity in existing projects, and a
need for more awareness of the various types of media bias to support
methodologically thorough performance evaluations of media bias detection
systems. Concluding from our analysis, we see the integration of recent machine
learning advancements with reliable and diverse bias assessment strategies from
other research areas as the most promising area for future research
contributions in the field.

媒体偏见检测是一个活跃的研究领域，基于计算方法和机器学习的分类方法让我们能够更准确地检测和分类各种类型的媒体偏见，然而，现有项目存在学科间的缺乏交叉和对多种媒体偏见类型的认识，未来的研究应该集成最新的机器学习进展和来自其他研究领域的可靠和多样化的偏见评估策略。

媒体偏见分类：关于媒体偏见形式和自动检测的系统文献综述

The Media Bias Taxonomy: A Systematic Literature Review on the Forms and  Automated Detection of Media Bias

Convolutional Neural Networks (CNNs) were recently shown to provide
state-of-the-art results for object category viewpoint estimation. However
different ways of formulating this problem have been proposed and the competing
approaches have been explored with very different design choices. This paper
presents a comparison of these approaches in a unified setting as well as a
detailed analysis of the key factors that impact performance. Followingly, we
present a new joint training method with the detection task and demonstrate its
benefit. We also highlight the superiority of classification approaches over
regression approaches, quantify the benefits of deeper architectures and
extended training data, and demonstrate that synthetic data is beneficial even
when using ImageNet training data. By combining all these elements, we
demonstrate an improvement of approximately 5% mAVP over previous
state-of-the-art results on the Pascal3D+ dataset. In particular for their most
challenging 24 view classification task we improve the results from 31.1% to
36.1% mAVP.

本文比较了不同的物体类别视点估计方法，并提出一种新的联合训练方法，并强调了分类方法的优越性、深度架构和扩展训练数据的好处，并证明了即使使用 ImageNet 训练数据，合成数据也是有益的。结合所有这些因素，我们在 Pascal3D + 数据集上的结果比先前的最新成果提高了约 5％的 mAVP，特别是在 24 个视点分类任务上，将结果从 31.1％提高到 36.1％的 mAVP。

构建多任务 CNN 进行视角估计

Crafting a multi-task CNN for viewpoint estimation

Many different machine learning algorithms exist; taking into account each
algorithm's hyperparameters, there is a staggeringly large number of possible
alternatives overall. We consider the problem of simultaneously selecting a
learning algorithm and setting its hyperparameters, going beyond previous work
that addresses these issues in isolation. We show that this problem can be
addressed by a fully automated approach, leveraging recent innovations in
Bayesian optimization. Specifically, we consider a wide range of feature
selection techniques (combining 3 search and 8 evaluator methods) and all
classification approaches implemented in WEKA, spanning 2 ensemble methods, 10
meta-methods, 27 base classifiers, and hyperparameter settings for each
classifier. On each of 21 popular datasets from the UCI repository, the KDD Cup
09, variants of the MNIST dataset and CIFAR-10, we show classification
performance often much better than using standard selection/hyperparameter
optimization methods. We hope that our approach will help non-expert users to
more effectively identify machine learning algorithms and hyperparameter
settings appropriate to their applications, and hence to achieve improved
performance.

使用贝叶斯优化和特征选择技术，全自动选择机器学习算法和设置其超参数，在 21 个数据集的分类表现中达到了比标准选择 / 超参数优化方法更好的结果。