Input distribution shift presents a significant problem in many real-world
systems. Here we present Xenovert, an adaptive algorithm that can dynamically
adapt to changes in input distribution. It is a perfect binary tree that
adaptively divides a continuous input space into several intervals of uniform
density while receiving a continuous stream of input. This process indirectly
maps the source distribution to the shifted target distribution, preserving the
data's relationship with the downstream decoder/operation, even after the shift
occurs. In this paper, we demonstrated how a neural network integrated with
Xenovert achieved better results in 4 out of 5 shifted datasets, saving the
hurdle of retraining a machine learning model. We anticipate that Xenovert can
be applied to many more applications that require adaptation to unforeseen
input distribution shifts, even when the distribution shift is drastic.

Xenovert 是一种自适应算法，可动态适应输入分布的变化，并通过将连续的输入空间自适应地划分为几个密度均匀的区间，间接地将源分布映射到变化后的目标分布，以实现数据与下游解码器 / 操作的关系保持不变。本文中，我们展示了整合 Xenovert 的神经网络在 5 个数据集中 4 个数据集上取得了更好的结果，避免了重新训练机器学习模型的困境。我们预计 Xenovert 可以应用于更多需要适应意想不到的输入分布变化的应用中，即使分布变化是剧烈的。

通过编码树与运动方程在实时中适应协变偏移

Adapting to Covariate Shift in Real-time by Encoding Trees with Motion  Equations

Input distribution shift is one of the vital problems in unsupervised domain
adaptation (UDA). The most popular UDA approaches focus on domain-invariant
representation learning, trying to align the features from different domains
into similar feature distributions. However, these approaches ignore the direct
alignment of input word distributions between domains, which is a vital factor
in word-level classification tasks such as cross-domain NER. In this work, we
shed new light on cross-domain NER by introducing a subword-level solution,
X-Piece, for input word-level distribution shift in NER. Specifically, we
re-tokenize the input words of the source domain to approach the target subword
distribution, which is formulated and solved as an optimal transport problem.
As this approach focuses on the input level, it can also be combined with
previous DIRL methods for further improvement. Experimental results show the
effectiveness of the proposed method based on BERT-tagger on four benchmark NER
datasets. Also, the proposed method is proved to benefit DIRL methods such as
DANN.

本研究提出了一种基于子词的解决方案（X-Piece），通过重新令牌化源域的输入单词以接近目标子词分布，来解决交叉域命名实体识别中的输入词级分布偏移问题，实验证明了这种方法基于 BERT-tagger 在四个基准 NER 数据集上的有效性。