面向领域泛化的源领域知识动态解码
Dynamically Decoding Source Domain Knowledge for Domain Generalization
摘要：
优化分类器对未知领域样本的性能仍然是一个具有挑战性的问题。虽然大多数现有的研究领域泛化集中在学习领域不变的特征表示，多专家框架已被提出作为一种可能的解决方案，并已表现出良好的性能。然而，目前的多专家学习框架未能充分利用源域知识在推理过程中，导致次优性能。在这项工作中，我们建议适应Transformers的目的是动态解码源域知识的领域泛化。具体来说，我们建立一个特定领域的本地专家每个源域和一个域不可知的特征分支作为查询。Transformer编码器将所有特定于域的特征编码为存储器中的源域知识。在Transformer解码器中，与域无关的查询与交叉注意模块中的存储器交互，并且与输入相似的域将对注意输出做出更多贡献。因此，源域知识得到动态解码，用于从看不见的域推断当前输入。该机制使所提出的方法能够很好地推广到看不见的领域。所提出的方法已在领域泛化领域的三个基准进行了评估，并显示出最好的性能相比，国家的最先进的方法。
英文摘要：
Optimizing the performance of classifiers on samples from unseen domains remains a challenging problem. While most existing studies on domain generalization focus on learning domain-invariant feature representations, multi-expert frameworks have been proposed as a possible solution and have demonstrated promising performance. However, current multi-expert learning frameworks fail to fully exploit source domain knowledge during inference, resulting in sub-optimal performance. In this work, we propose to adapt Transformers for the purpose of dynamically decoding source domain knowledge for domain generalization. Specifically, we build one domain-specific local expert per source domain and one domain-agnostic feature branch as query. A Transformer encoder encodes all domain-specific features as source domain knowledge in memory. In the Transformer decoder, the domain-agnostic query interacts with the memory in the cross-attention module, and domains that are similar to the input will contribute more to the attention output. Thus, source domain knowledge gets dynamically decoded for inference of the current input from unseen domain. This mechanism enables the proposed method to generalize well to unseen domains. The proposed method has been evaluated on three benchmarks in the domain generalization field and shown to have the best performance compared to state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
