论文标题
在无监督的域适应中采矿标签分布漂移
Mining Label Distribution Drift in Unsupervised Domain Adaptation
论文作者
论文摘要
无监督的域适应目标将与任务相关的知识从标记的源域转移到未标记的目标域。尽管已经做出了巨大的努力来最大程度地减少域的差异，但大多数现有方法仅通过使来自不同领域的特征表示形式对齐。除了数据分布的差异之外，源和目标标签分布之间的差距（被认为是标签分布漂移）是另一个关键因素升高域的差异，并且在探索不足。从这个角度来看，我们首先揭示标签分布漂移如何带来负面影响。接下来，我们建议标签分布匹配域对抗网络（LMDAN），以处理数据分布变化和标记分布漂移。在LMDAN中，标签分布漂移是通过源样本加权策略来解决的，该策略选择了有助于阳性适应性的样品，并避免样本不匹配的样本带来的不利影响。实验表明，LMDAN在可观的标签分布漂移下提供了卓越的性能。
Unsupervised domain adaptation targets to transfer task-related knowledge from labeled source domain to unlabeled target domain. Although tremendous efforts have been made to minimize domain divergence, most existing methods only partially manage by aligning feature representations from diverse domains. Beyond the discrepancy in data distribution, the gap between source and target label distribution, recognized as label distribution drift, is another crucial factor raising domain divergence, and has been under insufficient exploration. From this perspective, we first reveal how label distribution drift brings negative influence. Next, we propose Label distribution Matching Domain Adversarial Network (LMDAN) to handle data distribution shift and label distribution drift jointly. In LMDAN, label distribution drift is addressed by a source sample weighting strategy, which selects samples that contribute to positive adaptation and avoid adverse effects brought by the mismatched samples. Experiments show that LMDAN delivers superior performance under considerable label distribution drift.
