Methods for unsupervised domain adaptation (UDA) help to improve the
performance of deep neural networks on unseen domains without any labeled data.
Especially in medical disciplines such as histopathology, this is crucial since
large datasets with detailed annotations are scarce. While the majority of
existing UDA methods focus on the adaptation from a labeled source to a single
unlabeled target domain, many real-world applications with a long life cycle
involve more than one target domain. Thus, the ability to sequentially adapt to
multiple target domains becomes essential. In settings where the data from
previously seen domains cannot be stored, e.g., due to data protection
regulations, the above becomes a challenging continual learning problem. To
this end, we propose to use generative feature-driven image replay in
conjunction with a dual-purpose discriminator that not only enables the
generation of images with realistic features for replay, but also promotes
feature alignment during domain adaptation. We evaluate our approach
extensively on a sequence of three histopathological datasets for tissue-type
classification, achieving state-of-the-art results. We present detailed
ablation experiments studying our proposed method components and demonstrate a
possible use-case of our continual UDA method for an unsupervised patch-based
segmentation task given high-resolution tissue images.

本文介绍了一种基于生成式联合判别器的方法，能够连续自适应多个未标记的目标领域且保护隐私，特别适用于具有长生命周期的医学学科领域，实现了对组织类型分类取得了最先进的效果。

无标签域的多尺度特征对齐连续学习

Multi-scale Feature Alignment for Continual Learning of Unlabeled Domains

Unsupervised domain adaptation (uDA) models focus on pairwise adaptation
settings where there is a single, labeled, source and a single target domain.
However, in many real-world settings one seeks to adapt to multiple, but
somewhat similar, target domains. Applying pairwise adaptation approaches to
this setting may be suboptimal, as they fail to leverage shared information
among multiple domains. In this work we propose an information theoretic
approach for domain adaptation in the novel context of multiple target domains
with unlabeled instances and one source domain with labeled instances. Our
model aims to find a shared latent space common to all domains, while
simultaneously accounting for the remaining private, domain-specific factors.
Disentanglement of shared and private information is accomplished using a
unified information-theoretic approach, which also serves to establish a
stronger link between the latent representations and the observed data. The
resulting model, accompanied by an efficient optimization algorithm, allows
simultaneous adaptation from a single source to multiple target domains. We
test our approach on three challenging publicly-available datasets, showing
that it outperforms several popular domain adaptation methods.

本研究提出了一种信息理论方法来处理具有多个相似目标领域的无监督域适应问题，通过寻找一个共同的潜在空间，同时利用信息理论方法实现共享和私有信息的分离，并利用高效的优化算法完成从单一源到多个目标领域的自适应过程。

无监督多目标域自适应：信息论方法

Unsupervised Multi-Target Domain Adaptation: An Information Theoretic  Approach

Unsupervised domain adaptation (UDA) aims to learn the unlabeled target
domain by transferring the knowledge of the labeled source domain. To date,
most of the existing works focus on the scenario of one source domain and one
target domain (1S1T), and just a few works concern the scenario of multiple
source domains and one target domain (mS1T). While, to the best of our
knowledge, almost no work concerns the scenario of one source domain and
multiple target domains (1SmT), in which these unlabeled target domains may not
necessarily share the same categories, therefore, contrasting to mS1T, 1SmT is
more challenging. Accordingly, for such a new UDA scenario, we propose a UDA
framework through the model parameter adaptation (PA-1SmT). A key ingredient of
PA-1SmT is to transfer knowledge through adaptive learning of a common model
parameter dictionary, which is completely different from existing popular
methods for UDA, such as subspace alignment, distribution matching etc., and
can also be directly used for DA of privacy protection due to the fact that the
knowledge is transferred just via the model parameters rather than data itself.
Finally, our experimental results on three domain adaptation benchmark datasets
demonstrate the superiority of our framework.

本文提出了一种针对具有一个源域和多个目标域的无监督域适应场景 (1SmT) 的模型参数自适应转移的无监督域适应框架 (PA-1SmT)，该框架通过学习一个共同的模型参数词典进行知识转移，并在三个领域适应基准数据集上实验验证了其优越性。