Domain generalization (DG) is proposed to deal with the issue of domain
shift, which occurs when statistical differences exist between source and
target domains. However, most current methods do not account for a common
realistic scenario where the source and target domains have different classes.
To overcome this deficiency, open set domain generalization (OSDG) then emerges
as a more practical setting to recognize unseen classes in unseen domains. An
intuitive approach is to use multiple one-vs-all classifiers to define decision
boundaries for each class and reject the outliers as unknown. However, the
significant class imbalance between positive and negative samples often causes
the boundaries biased towards positive ones, resulting in misclassification for
known samples in the unseen target domain. In this paper, we propose a novel
meta-learning-based framework called dualistic MEta-learning with joint
DomaIn-Class matching (MEDIC), which considers gradient matching towards
inter-domain and inter-class splits simultaneously to find a generalizable
boundary balanced for all tasks. Experimental results demonstrate that MEDIC
not only outperforms previous methods in open set scenarios, but also maintains
competitive close set generalization ability at the same time. Our code is
available at this https URL

本文提出了一种基于元学习的新型框架，即双重 MEta-learning with joint DomaIn-Class matching（MEDIC），该框架同时考虑了领域间和类别间的梯度匹配，以找到适用于所有任务的平衡边界，实验证明该方法不仅在开放集场景中优于以前的方法，同时还保持了有竞争力的闭集泛化能力。

可泛化的决策边界：对开集领域泛化进行的二元元学习

Generalizable Decision Boundaries: Dualistic Meta-Learning for Open Set  Domain Generalization

The learning to defer (L2D) framework has the potential to make AI systems
safer. For a given input, the system can defer the decision to a human if the
human is more likely than the model to take the correct action. We study the
calibration of L2D systems, investigating if the probabilities they output are
sound. We find that Mozannar & Sontag's (2020) multiclass framework is not
calibrated with respect to expert correctness. Moreover, it is not even
guaranteed to produce valid probabilities due to its parameterization being
degenerate for this purpose. We propose an L2D system based on one-vs-all
classifiers that is able to produce calibrated probabilities of expert
correctness. Furthermore, our loss function is also a consistent surrogate for
multiclass L2D, like Mozannar & Sontag's (2020). Our experiments verify that
not only is our system calibrated, but this benefit comes at no cost to
accuracy. Our model's accuracy is always comparable (and often superior) to
Mozannar & Sontag's (2020) model's in tasks ranging from hate speech detection
to galaxy classification to diagnosis of skin lesions.

本研究提出新的基于 one-vs-all 分类器的 L2D 系统，旨在提高 L2D 系统的校准性能，这不会影响我们模型的准确性，同时在各种任务中也表现出可比较（通常是优于）Mozannar 和 Sontag（2020 年）模型的准确性，涵盖仇恨言论检测、星系分类和皮肤病诊断等。

使用一对多分类器的校准学习进行推迟

Calibrated Learning to Defer with One-vs-All Classifiers

Semi-supervised learning (SSL) is an effective means to leverage unlabeled
data to improve a model's performance. Typical SSL methods like FixMatch assume
that labeled and unlabeled data share the same label space. However, in
practice, unlabeled data can contain categories unseen in the labeled set,
i.e., outliers, which can significantly harm the performance of SSL algorithms.
To address this problem, we propose a novel Open-set Semi-Supervised Learning
(OSSL) approach called OpenMatch. Learning representations of inliers while
rejecting outliers is essential for the success of OSSL. To this end, OpenMatch
unifies FixMatch with novelty detection based on one-vs-all (OVA) classifiers.
The OVA-classifier outputs the confidence score of a sample being an inlier,
providing a threshold to detect outliers. Another key contribution is an
open-set soft-consistency regularization loss, which enhances the smoothness of
the OVA-classifier with respect to input transformations and greatly improves
outlier detection. OpenMatch achieves state-of-the-art performance on three
datasets, and even outperforms a fully supervised model in detecting outliers
unseen in unlabeled data on CIFAR10.

本文提出了一种称为 OpenMatch 的开放集半监督学习方法，该方法利用 novelty detection 基于 one-vs-all classifiers 来拒绝处理 unlabeled data 中的离群值，同时引入了开放集软一致性正则化损失来增强 OVA-classifier 对于输入变换的平滑性，并在三个数据集上实现了最先进的性能。