Open compound domain adaptation (OCDA) is a domain adaptation setting, where
target domain is modeled as a compound of multiple unknown homogeneous domains,
which brings the advantage of improved generalization to unseen domains. In
this work, we propose a principled meta-learning based approach to OCDA for
semantic segmentation, MOCDA, by modeling the unlabeled target domain
continuously. Our approach consists of four key steps. First, we cluster target
domain into multiple sub-target domains by image styles, extracted in an
unsupervised manner. Then, different sub-target domains are split into
independent branches, for which batch normalization parameters are learnt to
treat them independently. A meta-learner is thereafter deployed to learn to
fuse sub-target domain-specific predictions, conditioned upon the style code.
Meanwhile, we learn to online update the model by model-agnostic meta-learning
(MAML) algorithm, thus to further improve generalization. We validate the
benefits of our approach by extensive experiments on synthetic-to-real
knowledge transfer benchmark datasets, where we achieve the state-of-the-art
performance in both compound and open domains.

提出了一种基于元学习的 OCDA 方法 MOCDA，通过对未标记的目标域进行连续建模，利用图像样式将其划分为多个子目标域，同时在线使用 MAML 算法来更新模型，实现在合成到真实的知识转移基准数据集上取得最优性能的目的。

聚类，分裂，融合和更新：用于开放式复合领域自适应语义分割的元学习

Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain  Adaptive Semantic Segmentation

A central capability of intelligent systems is the ability to continuously
build upon previous experiences to speed up and enhance learning of new tasks.
Two distinct research paradigms have studied this question. Meta-learning views
this problem as learning a prior over model parameters that is amenable for
fast adaptation on a new task, but typically assumes the set of tasks are
available together as a batch. In contrast, online (regret based) learning
considers a sequential setting in which problems are revealed one after the
other, but conventionally train only a single model without any task-specific
adaptation. This work introduces an online meta-learning setting, which merges
ideas from both the aforementioned paradigms to better capture the spirit and
practice of continual lifelong learning. We propose the follow the meta leader
algorithm which extends the MAML algorithm to this setting. Theoretically, this
work provides an $\mathcal{O}(\log T)$ regret guarantee with only one
additional higher order smoothness assumption in comparison to the standard
online setting. Our experimental evaluation on three different large-scale
tasks suggest that the proposed algorithm significantly outperforms
alternatives based on traditional online learning approaches.

该研究提出了一种综合了元学习和在线学习范式的在线元学习模型，运用改进后的 MAML 算法，实现连续终身学习，实验结果表明该算法明显优于传统的在线学习方法。

在线元学习

Online Meta-Learning

Gradient-based meta-learners such as MAML are able to learn a meta-prior from
similar tasks to adapt to novel tasks from the same distribution with few
gradient updates. One important limitation of such frameworks is that they seek
a common initialization shared across the entire task distribution,
substantially limiting the diversity of the task distributions that they are
able to learn from. In this paper, we augment MAML with the capability to
identify tasks sampled from a multimodal task distribution and adapt quickly
through gradient updates. Specifically, we propose a multimodal MAML algorithm
that is able to modulate its meta-learned prior according to the identified
task, allowing faster adaptation. We evaluate the proposed model on a diverse
set of problems including regression, few-shot image classification, and
reinforcement learning. The results demonstrate the effectiveness of our model
in modulating the meta-learned prior in response to the characteristics of
tasks sampled from a multimodal distribution.

本文提出了一种基于多模态任务分布的 adaptative meta-learning 算法，并采用该算法来识别各种从多模态分布中采样的任务，以更快速地实现适应。结果表明该算法对于计算机视觉、强化学习和回归等多种任务领域都具有普适性。