Autonomous driving progress relies on large-scale annotated datasets. In this
work, we explore the potential of generative models to produce vast quantities
of freely-labeled data for autonomous driving applications and present
SubjectDrive, the first model proven to scale generative data production in a
way that could continuously improve autonomous driving applications. We
investigate the impact of scaling up the quantity of generative data on the
performance of downstream perception models and find that enhancing data
diversity plays a crucial role in effectively scaling generative data
production. Therefore, we have developed a novel model equipped with a subject
control mechanism, which allows the generative model to leverage diverse
external data sources for producing varied and useful data. Extensive
evaluations confirm SubjectDrive's efficacy in generating scalable autonomous
driving training data, marking a significant step toward revolutionizing data
production methods in this field.

自动驾驶进展依赖于大规模标注数据集。本研究探索生成模型在自动驾驶应用中产生大量自由标注数据的潜力，并提出了 SubjectDrive，这是第一个已证实能够持续改进自动驾驶应用的规模化生成数据生产模型。我们研究了扩大生成数据量对下游感知模型性能的影响，发现增强数据的多样性在有效扩展生成数据生产中起着关键作用。因此，我们开发了一种配备了主题控制机制的新型模型，使生成模型能够利用多元化的外部数据源产生多样且有用的数据。广泛的评估验证了 SubjectDrive 在生成可扩展自动驾驶训练数据方面的功效，标志着这一领域数据生产方法革命性的重要进展。

SubjectDrive：通过主体控制在自动驾驶中扩大生成数据规模

SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject  Control

In recent years, the availability of large-scale annotated datasets, such as
the Stanford Natural Language Inference and the Multi-Genre Natural Language
Inference, coupled with the advent of pre-trained language models, has
significantly contributed to the development of the natural language inference
domain. However, these crowdsourced annotated datasets often contain biases or
dataset artifacts, leading to overestimated model performance and poor
generalization. In this work, we focus on investigating dataset artifacts and
developing strategies to address these issues. Through the utilization of a
novel statistical testing procedure, we discover a significant association
between vocabulary distribution and text entailment classes, emphasizing
vocabulary as a notable source of biases. To mitigate these issues, we propose
several automatic data augmentation strategies spanning character to word
levels. By fine-tuning the ELECTRA pre-trained language model, we compare the
performance of boosted models with augmented data against their baseline
counterparts. The experiments demonstrate that the proposed approaches
effectively enhance model accuracy and reduce biases by up to 0.66% and 1.14%,
respectively.

通过利用一种新颖的统计测试方法，我们发现词汇分布与文本蕴含类之间存在显著关联，强调词汇是一种明显的偏差源。为了减轻这些问题，我们提出了几种自动数据增强策略，从字符到词级别。通过对 ELECTRA 预训练语言模型进行微调，我们将有增强数据的模型与基准模型进行了比较。实验证明，所提出的方法可以有效提高模型准确性，并分别减少偏差约 0.66% 和 1.14%。

通过统计检验和自动数据增强解析自然语言推理中的词汇偏见数据集以减少人为影响

Dissecting vocabulary biases datasets through statistical testing and  automated data augmentation for artifact mitigation in Natural Language  Inference