In semi-supervised domain adaptation (SSDA), a few labeled target samples of
each class help the model to transfer knowledge representation from the fully
labeled source domain to the target domain. Many existing methods ignore the
benefits of making full use of the labeled target samples from multi-level. To
make better use of this additional data, we propose a novel Prototype-based
Multi-level Learning (ProML) framework to better tap the potential of labeled
target samples. To achieve intra-domain adaptation, we first introduce a
pseudo-label aggregation based on the intra-domain optimal transport to help
the model align the feature distribution of unlabeled target samples and the
prototype. At the inter-domain level, we propose a cross-domain alignment loss
to help the model use the target prototype for cross-domain knowledge transfer.
We further propose a dual consistency based on prototype similarity and linear
classifier to promote discriminative learning of compact target feature
representation at the batch level. Extensive experiments on three datasets,
including DomainNet, VisDA2017, and Office-Home demonstrate that our proposed
method achieves state-of-the-art performance in SSDA.

本文提出了一种基于多级原型学习的半监督域自适应框架，采用伪标签聚合和交叉域对齐损失方法，以及通过原型相似度和线性分类器提升目标特征表示的判别性学习，实现了在三个数据集上卓越的 SSDA 性能。

基于原型多层学习的半监督域自适应

Semi-supervised Domain Adaptation via Prototype-based Multi-level  Learning

The standard class-incremental continual learning setting assumes a set of
tasks seen one after the other in a fixed and predefined order. This is not
very realistic in federated learning environments where each client works
independently in an asynchronous manner getting data for the different tasks in
time-frames and orders totally uncorrelated with the other ones. We introduce a
novel federated learning setting (AFCL) where the continual learning of
multiple tasks happens at each client with different orderings and in
asynchronous time slots. We tackle this novel task using prototype-based
learning, a representation loss, fractal pre-training, and a modified
aggregation policy. Our approach, called FedSpace, effectively tackles this
task as shown by the results on the CIFAR-100 dataset using 3 different
federated splits with 50, 100, and 500 clients, respectively. The code and
federated splits are available at this https URL

本文提出了一种名为 FedSpace 的解决异步时间段下面向多任务连续学习问题的方法，它基于原型学习、表示损失、分形预训练和改进聚合策略，在 Cifar-100 数据集上得到了有效的应用。