Few-shot learning (FSL) aims to recognize novel queries with only a few
support samples through leveraging prior knowledge from a base dataset. In this
paper, we consider the domain shift problem in FSL and aim to address the
domain gap between the support set and the query set. Different from previous
cross-domain FSL work (CD-FSL) that considers the domain shift between base and
novel classes, the new problem, termed cross-domain cross-set FSL (CDSC-FSL),
requires few-shot learners not only to adapt to the new domain, but also to be
consistent between different domains within each novel class. To this end, we
propose a novel approach, namely stabPA, to learn prototypical compact and
cross-domain aligned representations, so that the domain shift and few-shot
learning can be addressed simultaneously. We evaluate our approach on two new
CDCS-FSL benchmarks built from the DomainNet and Office-Home datasets
respectively. Remarkably, our approach outperforms multiple elaborated
baselines by a large margin, e.g., improving 5-shot accuracy by 6.0 points on
average on DomainNet. Code is available at
this https URL

本文提出一种名为 stabPA 的新方法，通过学习紧凑的原型和跨域对齐表示来同时解决域漂移和少样本学习问题，并在 DomainNet 数据集上实现了优异的结果。

跨域跨集合少样本学习通过学习紧凑和对齐表征

Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and  Aligned Representations

Recent works of multi-source domain adaptation focus on learning a
domain-agnostic model, of which the parameters are static. However, such a
static model is difficult to handle conflicts across multiple domains, and
suffers from a performance degradation in both source domains and target
domain. In this paper, we present dynamic transfer to address domain conflicts,
where the model parameters are adapted to samples. The key insight is that
adapting model across domains is achieved via adapting model across samples.
Thus, it breaks down source domain barriers and turns multi-source domains into
a single-source domain. This also simplifies the alignment between source and
target domains, as it only requires the target domain to be aligned with any
part of the union of source domains. Furthermore, we find dynamic transfer can
be simply modeled by aggregating residual matrices and a static convolution
matrix. Experimental results show that, without using domain labels, our
dynamic transfer outperforms the state-of-the-art method by more than 3% on the
large multi-source domain adaptation datasets -- DomainNet. Source code is at
this https URL

本文提出动态转移 (Dynamic Transfer) 方法应对多源领域间的冲突，它能够通过对样本进行模型参数的适应性调整，突破领域界限，简化源域和目标域之间的对齐。实验结果表明，我们提出的动态转移方法在多源领域适应数据集 (DomainNet) 上可以将性能提升超过 3%。

多源域适应的动态传递

Dynamic Transfer for Multi-Source Domain Adaptation

We introduce Domain-specific Masks for Generalization, a model for improving
both in-domain and out-of-domain generalization performance. For domain
generalization, the goal is to learn from a set of source domains to produce a
single model that will best generalize to an unseen target domain. As such,
many prior approaches focus on learning representations which persist across
all source domains with the assumption that these domain agnostic
representations will generalize well. However, often individual domains contain
characteristics which are unique and when leveraged can significantly aid
in-domain recognition performance. To produce a model which best generalizes to
both seen and unseen domains, we propose learning domain specific masks. The
masks are encouraged to learn a balance of domain-invariant and domain-specific
features, thus enabling a model which can benefit from the predictive power of
specialized features while retaining the universal applicability of
domain-invariant features. We demonstrate competitive performance compared to
naive baselines and state-of-the-art methods on both PACS and DomainNet.

本研究提出了一种域特定掩码模型，旨在提高领域内和领域外的泛化性能，在 PACS 和 DomainNet 中实现与先前的方法竞争性能。