论文标题
以乐观和一致性来应对无监督的多源域的适应
Tackling unsupervised multi-source domain adaptation with optimism and consistency
论文作者
论文摘要
一段时间以来，已知多源域适应的问题可以被视为单个源域适应任务，其中源域对应于原始源域的混合物。尽管如此，如何调整混合物分配权重仍然是一个悬而未决的问题。此外，该主题上的大多数现有工作仅着重于最大程度地减少源域上的错误并实现域名表示，这不足以确保目标域上的较低错误。在这项工作中，我们提出了一个新颖的框架，该框架通过使用温和乐观的目标函数和目标样本上的一致性正则化来解决问题并击败当前的艺术状态。
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.
