让模型从多个专家中学习，提升跨域泛化能力。
LFME: A Simple Framework for Learning from Multiple Experts in Domain Generalization
- 训练多个领域专家，用其输出指导主模型。
- 在多个基准上显著提升基线性能，媲美先进方法。
- 适合需要强泛化能力的跨域学习任务。
领域泛化（DG）旨在利用多个源域数据，在未见目标域上保持良好性能。尽管部分方法取得成功，但普遍提升仍具挑战。本文提出简单有效的框架LFME，使目标模型在所有源域上成为专家。除训练推理用的目标模型外，还训练多个专注不同领域的专家，通过正则化目标模型的对数几率输出，获得专家的指导。深入分析发现，该正则化项隐式增强了目标模型的信息利用能力，并在训练中挖掘难样本。大量实验表明，LFME持续改进基线，在多个不同任务的基准上达到与现有先进方法相当的性能。代码已公开于https://github.com/liangchen527/LFME。
原文摘要 · Abstract (English)
Domain generalization (DG) methods aim to maintain good performance in an unseen target domain by using training data from multiple source domains. While success on certain occasions are observed, enhancing the baseline across most scenarios remains challenging. This work introduces a simple yet effective framework, dubbed learning from multiple experts (LFME), that aims to make the target model an expert in all source domains to improve DG. Specifically, besides learning the target model used in inference, LFME will also train multiple experts specialized in different domains, whose output probabilities provide professional guidance by simply regularizing the logit of the target model. Delving deep into the framework, we reveal that the introduced logit regularization term implicitly provides effects of enabling the target model to harness more information, and mining hard samples from the experts during training. Extensive experiments on benchmarks from different DG tasks demonstrate that LFME is consistently beneficial to the baseline and can achieve comparable performance to existing arts. Code is available at~\url{https://github.com/liangchen527/LFME}.
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