We show that content on the web is often translated into many languages, and
the low quality of these multi-way translations indicates they were likely
created using Machine Translation (MT). Multi-way parallel, machine generated
content not only dominates the translations in lower resource languages; it
also constitutes a large fraction of the total web content in those languages.
We also find evidence of a selection bias in the type of content which is
translated into many languages, consistent with low quality English content
being translated en masse into many lower resource languages, via MT. Our work
raises serious concerns about training models such as multilingual large
language models on both monolingual and bilingual data scraped from the web.

Web 内容通常被翻译成多种语言，多语言机器翻译常常质量较低，且低资源语言中占大部分的内容是机器生成的多语言内容；我们发现内容选择偏差，即低质量英文内容通过机器翻译大规模转译成较低资源语言；我们的研究对使用从网上刮取的单语和双语数据来训练多语言大型语言模型等模型提出了严重的疑虑。

网络中机器翻译的惊人数量：多样并行的洞察

A Shocking Amount of the Web is Machine Translated: Insights from  Multi-Way Parallelism

In this paper, we tackle the challenge of face recognition in the wild, where
images often suffer from low quality and real-world distortions. Traditional
heuristic approaches-either training models directly on these degraded images
or their enhanced counterparts using face restoration techniques-have proven
ineffective, primarily due to the degradation of facial features and the
discrepancy in image domains. To overcome these issues, we propose an effective
adapter for augmenting existing face recognition models trained on high-quality
facial datasets. The key of our adapter is to process both the unrefined and
the enhanced images by two similar structures where one is fixed and the other
trainable. Such design can confer two benefits. First, the dual-input system
minimizes the domain gap while providing varied perspectives for the face
recognition model, where the enhanced image can be regarded as a complex
non-linear transformation of the original one by the restoration model. Second,
both two similar structures can be initialized by the pre-trained models
without dropping the past knowledge. The extensive experiments in zero-shot
settings show the effectiveness of our method by surpassing baselines of about
3%, 4%, and 7% in three datasets. Our code will be publicly available at
this https URL

在这篇论文中，我们致力于解决野外人脸识别的挑战，其中图片常常存在质量低下和真实世界的扭曲。通过提出一种适用于基于高质量数据集上训练的现有人脸识别模型的有效适配器，我们旨在克服传统启发式方法在处理低质量图片时的无效性。

野外人脸识别的有效适配器

Effective Adapter for Face Recognition in the Wild

Deepfake has recently raised a plethora of societal concerns over its
possible security threats and dissemination of fake information. Much research
on deepfake detection has been undertaken. However, detecting low quality as
well as simultaneously detecting different qualities of deepfakes still remains
a grave challenge. Most SOTA approaches are limited by using a single specific
model for detecting certain deepfake video quality type. When constructing
multiple models with prior information about video quality, this kind of
strategy incurs significant computational cost, as well as model and training
data overhead. Further, it cannot be scalable and practical to deploy in
real-world settings. In this work, we propose a universal intra-model
collaborative learning framework to enable the effective and simultaneous
detection of different quality of deepfakes. That is, our approach is the
quality-agnostic deepfake detection method, dubbed QAD . In particular, by
observing the upper bound of general error expectation, we maximize the
dependency between intermediate representations of images from different
quality levels via Hilbert-Schmidt Independence Criterion. In addition, an
Adversarial Weight Perturbation module is carefully devised to enable the model
to be more robust against image corruption while boosting the overall model's
performance. Extensive experiments over seven popular deepfake datasets
demonstrate the superiority of our QAD model over prior SOTA benchmarks.

我们提出了一种通用的模型内协作学习框架，实现了对不同质量的 Deepfake 的有效和同时检测，称为 QAD 模型。通过观察普通误差期望的上限，通过 Hilbert-Schmidt 独立准则最大化不同质量级别的图像的中间表示之间的依赖关系。此外，我们还精心设计了对抗性权重扰动模块，使得模型在提升整体性能的同时更加稳健，能够抵抗图像的损坏。对七个流行的 Deepfake 数据集进行了大量实验证明了我们 QAD 模型超越了之前的 SOTA 基准。