Achieving high accuracy on data from domains unseen during training is a
fundamental challenge in domain generalization (DG). While state-of-the-art DG
classifiers have demonstrated impressive performance across various tasks, they
have shown a bias towards domain-dependent information, such as image styles,
rather than domain-invariant information, such as image content. This bias
renders them unreliable for deployment in risk-sensitive scenarios such as
autonomous driving where a misclassification could lead to catastrophic
consequences. To enable risk-averse predictions from a DG classifier, we
propose a novel inference procedure, Test-Time Neural Style Smoothing (TT-NSS),
that uses a "style-smoothed" version of the DG classifier for prediction at
test time. Specifically, the style-smoothed classifier classifies a test image
as the most probable class predicted by the DG classifier on random
re-stylizations of the test image. TT-NSS uses a neural style transfer module
to stylize a test image on the fly, requires only black-box access to the DG
classifier, and crucially, abstains when predictions of the DG classifier on
the stylized test images lack consensus. Additionally, we propose a neural
style smoothing (NSS) based training procedure that can be seamlessly
integrated with existing DG methods. This procedure enhances prediction
consistency, improving the performance of TT-NSS on non-abstained samples. Our
empirical results demonstrate the effectiveness of TT-NSS and NSS at producing
and improving risk-averse predictions on unseen domains from DG classifiers
trained with SOTA training methods on various benchmark datasets and their
variations.

本论文提出了一种基于测试时间神经风格平滑和神经风格平滑训练程序的领域泛化风险规避分类器，该分类器使用神经风格转移模块实现实时的图片风格调整，并在测试时利用黑盒访问领域泛化分类器，通过对多种风格的图像的识别结果进行综合以提高预测抗风险的能力，并在已有领域泛化方法中具有可无缝集成性。

基于风险厌恶的领域泛化快速神经风格平滑

On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization

We would like to learn a representation of the data which decomposes an
observation into factors of variation which we can independently control.
Specifically, we want to use minimal supervision to learn a latent
representation that reflects the semantics behind a specific grouping of the
data, where within a group the samples share a common factor of variation. For
example, consider a collection of face images grouped by identity. We wish to
anchor the semantics of the grouping into a relevant and disentangled
representation that we can easily exploit. However, existing deep probabilistic
models often assume that the observations are independent and identically
distributed. We present the Multi-Level Variational Autoencoder (ML-VAE), a new
deep probabilistic model for learning a disentangled representation of a set of
grouped observations. The ML-VAE separates the latent representation into
semantically meaningful parts by working both at the group level and the
observation level, while retaining efficient test-time inference. Quantitative
and qualitative evaluations show that the ML-VAE model (i) learns a
semantically meaningful disentanglement of grouped data, (ii) enables
manipulation of the latent representation, and (iii) generalises to unseen
groups.

提出了 Multi-Level Variational Autoencoder 用于学习语义上相关并解耦的潜变量表示，将工作集中在组别和个体水平上的同时保持高效的测试时推理，以便对相关变量进行无监督控制。