It has been known for a while that the problem of multi-source domain adaptation can be regarded as a single source domain adaptation task where the source domain corresponds to a mixture of the original source domains. Nonetheless, how to adjust the mixture distribution weights remains an open question. Moreover, most existing work on this topic focuses only on minimizing the error on the source domains and achieving domain-invariant representations, which is insufficient to ensure low error on the target domain. In this work, we present a novel framework that addresses both problems and beats the current state of the art by using a mildly optimistic objective function and consistency regularization on the target samples.

中文翻译：

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以乐观和一致性解决无监督多源域适应问题

众所周知，多源域自适应问题可以看作是单源域自适应任务，其中源域对应于原始源域的混合。尽管如此，如何调整混合分布权重仍然是一个悬而未决的问题。此外，关于该主题的大多数现有工作仅关注最小化源域上的错误并实现域不变表示，这不足以确保目标域上的低错误。在这项工作中，我们提出了一个新颖的框架，该框架通过对目标样​​本使用温和的乐观目标函数和一致性正则化来解决这两个问题并击败当前最先进的技术。