Existing domain generalization aims to learn a generalizable model to perform
well even on unseen domains. For many real-world machine learning applications,
the data distribution often shifts gradually along domain indices. For example,
a self-driving car with a vision system drives from dawn to dusk, with the sky
darkening gradually. Therefore, the system must be able to adapt to changes in
ambient illumination and continue to drive safely on the road. In this paper,
we formulate such problems as Evolving Domain Generalization, where a model
aims to generalize well on a target domain by discovering and leveraging the
evolving pattern of the environment. We then propose Directional Domain
Augmentation (DDA), which simulates the unseen target features by mapping
source data as augmentations through a domain transformer. Specifically, we
formulate DDA as a bi-level optimization problem and solve it through a novel
meta-learning approach in the representation space. We evaluate the proposed
method on both synthetic datasets and realworld datasets, and empirical results
show that our approach can outperform other existing methods.

本文提出了一种新的演化域广义化方法，即 Directional Domain Augmentation (DDA)，并采用表示空间中的元学习方法解决了双层优化问题，该方法在合成数据集和现实世界数据集上进行了实际测试，并取得优于其他现有方法的结果。

预测你将学到什么：非平稳环境下领域泛化的数据增强

Foresee What You Will Learn: Data Augmentation for Domain Generalization in Non-stationary Environment

Domain generalization aims to improve the generalization capability of
machine learning systems to out-of-distribution (OOD) data. Existing domain
generalization techniques embark upon stationary and discrete environments to
tackle the generalization issue caused by OOD data. However, many real-world
tasks in non-stationary environments (e.g. self-driven car system, sensor
measures) involve more complex and continuously evolving domain drift, which
raises new challenges for the problem of domain generalization. In this paper,
we formulate the aforementioned setting as the problem of evolving domain
generalization. Specifically, we propose to introduce a probabilistic framework
called Latent Structure-aware Sequential Autoencoder (LSSAE) to tackle the
problem of evolving domain generalization via exploring the underlying
continuous structure in the latent space of deep neural networks, where we aim
to identify two major factors namely covariate shift and concept shift
accounting for distribution shift in non-stationary environments. Experimental
results on both synthetic and real-world datasets show that LSSAE can lead to
superior performances based on the evolving domain generalization setting.

本文提出了一种基于概率框架的隐变量结构自然语言生成器，通过探索深度神经网络中的潜在连续结构来解决不断演化的域泛化问题，特别是在处理非平稳和连续漂移的任务中，可以通过识别协变量漂移和概念漂移，为域泛化问题带来优越的性能。