Unsupervised domain adaption (UDA) aims to adapt models learned from a
well-annotated source domain to a target domain, where only unlabeled samples
are given. Current UDA approaches learn domain-invariant features by aligning
source and target feature spaces. Such alignments are imposed by constraints
such as statistical discrepancy minimization or adversarial training. However,
these constraints could lead to the distortion of semantic feature structures
and loss of class discriminability. In this paper, we introduce a novel prompt
learning paradigm for UDA, named Domain Adaptation via Prompt Learning (DAPL).
In contrast to prior works, our approach makes use of pre-trained
vision-language models and optimizes only very few parameters. The main idea is
to embed domain information into prompts, a form of representations generated
from natural language, which is then used to perform classification. This
domain information is shared only by images from the same domain, thereby
dynamically adapting the classifier according to each domain. By adopting this
paradigm, we show that our model not only outperforms previous methods on
several cross-domain benchmarks but also is very efficient to train and easy to
implement.

本文提出了一种新颖的无监督领域自适应学习范式 —— 通过提示学习的领域自适应 (DAPL)，通过嵌入域信息和利用预训练的视觉 - 语言模型进行分类，此方法在多个领域的基准测试数据上表现优异，并且训练效率高、易于实现。

基于提示学习的领域适应

Domain Adaptation via Prompt Learning

We study the problem of few-shot graph classification across domains with
nonequivalent feature spaces by introducing three new cross-domain benchmarks
constructed from publicly available datasets. We also propose an
attention-based graph encoder that uses three congruent views of graphs, one
contextual and two topological views, to learn representations of task-specific
information for fast adaptation, and task-agnostic information for knowledge
transfer. We run exhaustive experiments to evaluate the performance of
contrastive and meta-learning strategies. We show that when coupled with
metric-based meta-learning frameworks, the proposed encoder achieves the best
average meta-test classification accuracy across all benchmarks. The source
code and data will be released here: this https URL

本文提出了一种用于跨领域的小样本图分类问题的基于注意力机制的图编码器，使用三个具有相同结构但不同功能的图像来学习任务特定的信息表示以达到快速适应的目的，实验结果表明，该编码器结合度量学习元学习框架时，可以在所有基准测试中实现最佳的平均元测试分类准确性。

跨域少样本图分类

Cross-Domain Few-Shot Graph Classification

Conventional unsupervised domain adaptation (UDA) studies the knowledge
transfer between a limited number of domains. This neglects the more practical
scenario where data are distributed in numerous different domains in the real
world. The domain similarity between those domains is critical for domain
adaptation performance. To describe and learn relations between different
domains, we propose a novel Domain2Vec model to provide vectorial
representations of visual domains based on joint learning of feature
disentanglement and Gram matrix. To evaluate the effectiveness of our
Domain2Vec model, we create two large-scale cross-domain benchmarks. The first
one is TinyDA, which contains 54 domains and about one million MNIST-style
images. The second benchmark is DomainBank, which is collected from 56 existing
vision datasets. We demonstrate that our embedding is capable of predicting
domain similarities that match our intuition about visual relations between
different domains. Extensive experiments are conducted to demonstrate the power
of our new datasets in benchmarking state-of-the-art multi-source domain
adaptation methods, as well as the advantage of our proposed model.

本研究提出了一种基于 Domain2Vec 模型进行视觉领域向量化表示的方法，并创建了两个大规模跨域基准测试集 TinyDA 和  DomainBank，实验表明该方法可以预测不同域之间的相似性，并优于现有领先的多源领域自适应方法。