Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation

Learning to Combine: Knowledge Aggregation for Multi-Source Domain  Adaptation | Papers With Code

Transferring knowledges learned from multiple source domains to target domain is a more practical and challenging task than conventional single-source domain adaptation. Furthermore, the increase of modalities brings more difficulty in aligning feature distributions among multiple domains. To mitigate these problems, we propose a Learning to Combine for Multi-Source Domain Adaptation (LtC-MSDA) framework
via exploring interactions among domains. In the nutshell, a knowledge graph is constructed on the prototypes of various domains to realize the information propagation among semantically adjacent representations. On such basis, a graph model is learned to predict query samples under the guidance of correlated prototypes. In addition, we design a Relation Alignment Loss (RAL) to facilitate the consistency of categories’ relational interdependency and the compactness of features, which boosts
features’ intra-class invariance and inter-class separability. Comprehensive results on public benchmark datasets demonstrate that our approach outperforms existing methods with a remarkable margin.

相比于传统的单源领域适应,迁移来自于多个源领域的知识到目标领域是更实际和有挑战性的人物。进一步,模态的增加导致连结多个领域的特征分布更加困难。为了解决这些问题,我们提出了一个用于多源域适应的框架,通过探索域之间的交叉。本质上,一个知识图被构建于各种域的原型之上,去实现语义连接表示之间的信息传递。在以上基础之上,本在相关的原型的指导下一个图模型被学习,以预测查询样本。之外,我们设计了一个关系连接损失以保持类别之间相互依存的一致性和特征的紧密,这个损失可以促进特征的类内不变性和类间可区分性。

论文地址:https://arxiv.org/pdf/2007.08801.pdf

代码地址:https://github.com/ChrisAllenMing/LtC-MSDA

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