Domain invariant learning aims to learn models that extract invariant
features over various training domains, resulting in better generalization to
unseen target domains. Recently, Bayesian Neural Networks have achieved
promising results in domain invariant learning, but most works concentrate on
aligning features distributions rather than parameter distributions. Inspired
by the principle of Bayesian Neural Network, we attempt to directly learn the
domain invariant posterior distribution of network parameters. We first propose
a theorem to show that the invariant posterior of parameters can be implicitly
inferred by aggregating posteriors on different training domains. Our
assumption is more relaxed and allows us to extract more domain invariant
information. We also propose a simple yet effective method, named PosTerior
Generalization (PTG), that can be used to estimate the invariant parameter
distribution. PTG fully exploits variational inference to approximate parameter
distributions, including the invariant posterior and the posteriors on training
domains. Furthermore, we develop a lite version of PTG for widespread
applications. PTG shows competitive performance on various domain
generalization benchmarks on DomainBed. Additionally, PTG can use any existing
domain generalization methods as its prior, and combined with previous
state-of-the-art method the performance can be further improved. Code will be
made public.

通过聚合不同训练领域上的后验，本研究提出了一个引理来隐式推断参数的不变后验分布，并使用 Posterior Generalization (PTG) 方法来估计不变参数分布，该方法基于变分推断近似参数分布，包括不变后验和训练领域的后验。研究结果表明，PTG 在 DomainBed 上的各种领域泛化基准测试上表现出竞争性能，且可与现有的领域泛化方法相结合以进一步提高性能。

基于贝叶斯的领域不变学习通过参数分布的后验概括

Bayesian Domain Invariant Learning via Posterior Generalization of  Parameter Distributions

The human visual system is remarkable in learning new visual concepts from
just a few examples. This is precisely the goal behind few-shot class
incremental learning (FSCIL), where the emphasis is additionally placed on
ensuring the model does not suffer from "forgetting". In this paper, we push
the boundary further for FSCIL by addressing two key questions that bottleneck
its ubiquitous application (i) can the model learn from diverse modalities
other than just photo (as humans do), and (ii) what if photos are not readily
accessible (due to ethical and privacy constraints). Our key innovation lies in
advocating the use of sketches as a new modality for class support. The product
is a "Doodle It Yourself" (DIY) FSCIL framework where the users can freely
sketch a few examples of a novel class for the model to learn to recognize
photos of that class. For that, we present a framework that infuses (i)
gradient consensus for domain invariant learning, (ii) knowledge distillation
for preserving old class information, and (iii) graph attention networks for
message passing between old and novel classes. We experimentally show that
sketches are better class support than text in the context of FSCIL, echoing
findings elsewhere in the sketching literature.

本文提出了一种名为 'Doodle It Yourself' 的基于草图的 few-shot class incremental learning 框架，利用多种模式使模型学习到更多的新概念。实验表明，相比于文本，草图更适合用作支持样本。