LiDAR semantic segmentation for autonomous driving has been a growing field
of interest in the past few years. Datasets and methods have appeared and
expanded very quickly, but methods have not been updated to exploit this new
availability of data and continue to rely on the same classical datasets.
Different ways of performing LIDAR semantic segmentation training and
inference can be divided into several subfields, which include the following:
domain generalization, the ability to segment data coming from unseen domains ;
source-to-source segmentation, the ability to segment data coming from the
training domain; and pre-training, the ability to create re-usable geometric
primitives.
In this work, we aim to improve results in all of these subfields with the
novel approach of multi-source training. Multi-source training relies on the
availability of various datasets at training time and uses them together rather
than relying on only one dataset.
To overcome the common obstacles found for multi-source training, we
introduce the coarse labels and call the newly created multi-source dataset
COLA. We propose three applications of this new dataset that display systematic
improvement over single-source strategies: COLA-DG for domain generalization
(up to +10%), COLA-S2S for source-to-source segmentation (up to +5.3%), and
COLA-PT for pre-training (up to +12%).

通过多源训练的新方法，使用 COLA 数据集，在 LiDAR 语义分割领域的几个子领域（包括域泛化、源到源分割和预训练）中得到了系统性的改进。

COLA: 自动驾驶中的多源粗标签激光雷达语义分割

COLA: COarse-LAbel multi-source LiDAR semantic segmentation for  autonomous driving

We present a cross-domain approach for automated measurement and context
extraction based on pre-trained language models. We construct a multi-source,
multi-domain corpus and train an end-to-end extraction pipeline. We then apply
multi-source task-adaptive pre-training and fine-tuning to benchmark the
cross-domain generalization capability of our model. Further, we conceptualize
and apply a task-specific error analysis and derive insights for future work.
Our results suggest that multi-source training leads to the best overall
results, while single-source training yields the best results for the
respective individual domain. While our setup is successful at extracting
quantity values and units, more research is needed to improve the extraction of
contextual entities. We make the cross-domain corpus used in this work
available online.

我们提出了一种基于预训练语言模型的跨领域自动测量和上下文提取方法，并通过构建多源多领域语料库和训练端到端提取流水线，应用多源任务自适应预训练和微调方法来评估模型的跨领域泛化能力，并通过任务特定的误差分析得出未来工作的见解。我们的结果表明，多源训练导致最佳的整体结果，而针对各自单个领域的训练则产生最佳的结果。虽然我们的设置在提取数量值和单位方面取得了成功，但仍需进一步研究以改进上下文实体的提取。我们将本文中使用的跨领域语料库在线提供。

跨领域测量、单元和上下文提取的多源 (预) 训练

Multi-Source (Pre-)Training for Cross-Domain Measurement, Unit and  Context Extraction

We propose a novel data-augmentation technique for neural machine translation
based on ROT-$k$ ciphertexts. ROT-$k$ is a simple letter substitution cipher
that replaces a letter in the plaintext with the $k$th letter after it in the
alphabet. We first generate multiple ROT-$k$ ciphertexts using different values
of $k$ for the plaintext which is the source side of the parallel data. We then
leverage this enciphered training data along with the original parallel data
via multi-source training to improve neural machine translation. Our method,
CipherDAug, uses a co-regularization-inspired training procedure, requires no
external data sources other than the original training data, and uses a
standard Transformer to outperform strong data augmentation techniques on
several datasets by a significant margin. This technique combines easily with
existing approaches to data augmentation, and yields particularly strong
results in low-resource settings.

该研究提出了一种新的数据增强技术，基于 ROT - k 密码文本。通过使用 ROT - k 加密训练数据与原始数据一起进行多源训练，我们的方法 CipherDAug 使用协同正则化风格的训练过程，在低资源环境中比强数据增强技术表现得更好。