To ensure the out-of-distribution (OOD) generalization performance,
traditional domain generalization (DG) methods resort to training on data from
multiple sources with different underlying distributions. And the success of
those DG methods largely depends on the fact that there are diverse training
distributions. However, it usually needs great efforts to obtain enough
heterogeneous data due to the high expenses, privacy issues or the scarcity of
data. Thus an interesting yet seldom investigated problem arises: how to
improve the OOD generalization performance when the perceived heterogeneity is
limited. In this paper, we instantiate a new framework called few-domain
generalization (FDG), which aims to learn a generalizable model from very few
domains of novel tasks with the knowledge acquired from previous learning
experiences on base tasks. Moreover, we propose a Meta Adaptive Task Sampling
(MATS) procedure to differentiate base tasks according to their semantic and
domain-shift similarity to the novel task. Empirically, we show that the newly
introduced FDG framework can substantially improve the OOD generalization
performance on the novel task and further combining MATS with episodic training
could outperform several state-of-the-art DG baselines on widely used
benchmarks like PACS and DomainNet.

本文研究针对少量异构数据的 out-of-distribution （OOD）泛化性能，提出一种新的 few-domain generalization (FDG) 框架，并结合元适应性任务采样（MATS）和情节化训练（episodic training）方法，显著提高了在 PACS 和 DomainNet 等广泛使用的基准测试中的 OOD 泛化性能。

适应性元任务采样实现少领域泛化

Meta Adaptive Task Sampling for Few-Domain Generalization

Despite achieving state-of-the-art zero-shot performance, existing
vision-language models still fall short of few-shot transfer ability on
domain-specific problems. Classical fine-tuning often fails to prevent highly
expressive models from exploiting spurious correlations. Although
model-agnostic meta-learning (MAML) presents as a natural alternative for
few-shot transfer learning, the expensive computation due to implicit
second-order optimization limits its use on large-scale vision-language models
such as CLIP. While much literature has been devoted to exploring alternative
optimization strategies, we identify another essential aspect towards effective
few-shot transfer learning, task sampling, which is previously only be viewed
as part of data pre-processing in MAML. To show the impact of task sampling, we
propose a simple algorithm, Model-Agnostic Multitask Fine-tuning (MAMF), which
differentiates classical fine-tuning only on uniformly sampling multiple tasks.
Despite its simplicity, we show that MAMF consistently outperforms classical
fine-tuning on five few-shot vision-language classification tasks. We further
show that the effectiveness of the bi-level optimization in MAML is highly
sensitive to the zero-shot performance of a task in the context of few-shot
vision-language classification. The goal of this paper is to provide new
insights on what makes few-shot learning work, and encourage more research into
investigating better task sampling strategies.

提出了一种新的模型无关多任务微调算法（MAMF）并探究了任务抽样对于有效进行少样本学习的影响，表明 MAMF 在五个少样本视觉语言分类任务上表现比经典的微调更佳，旨在为少样本学习提供新的见解，并鼓励探究更好的任务抽样策略。