论文标题
无源数据的域名适应
Domain Adaptation without Source Data
论文作者
论文摘要
域的适应性假设来自源和目标域中的样品在训练阶段可以自由访问。但是，这种假设在现实世界中很少是合理的，并且可能导致数据私人关系问题，尤其是当源域的标签可以作为标识符的敏感属性时。为了避免访问可能包含敏感信息的源数据，我们介绍了无源数据域的适应（SFDA）。我们的关键想法是利用源域中的预训练模型，并以自学方式逐步更新目标模型。我们观察到，通过预训练的源模型测量的具有较低自我镜的目标样品更有可能正确分类。由此，我们选择具有自我注入标准的可靠样品，并将其定义为类原型。然后，我们根据与类原型的相似性分数为每个目标样本分配伪标签。此外，为了减少伪标记过程的不确定性，我们提出了基于设定的基于距离的过滤，这不需要任何可调的超参数。最后，我们使用过滤的伪标签训练目标模型，并通过预先训练的源模型进行正则化。令人惊讶的是，如果没有直接使用标记的源样本，我们的PRDA在基准数据集上的表现优于常规域的适应方法。我们的代码可在https://github.com/youngryan1993/sfda-sourcefreeda上公开获取
Domain adaptation assumes that samples from source and target domains are freely accessible during a training phase. However, such an assumption is rarely plausible in the real-world and possibly causes data-privacy issues, especially when the label of the source domain can be a sensitive attribute as an identifier. To avoid accessing source data that may contain sensitive information, we introduce Source data-Free Domain Adaptation (SFDA). Our key idea is to leverage a pre-trained model from the source domain and progressively update the target model in a self-learning manner. We observe that target samples with lower self-entropy measured by the pre-trained source model are more likely to be classified correctly. From this, we select the reliable samples with the self-entropy criterion and define these as class prototypes. We then assign pseudo labels for every target sample based on the similarity score with class prototypes. Furthermore, to reduce the uncertainty from the pseudo labeling process, we propose set-to-set distance-based filtering which does not require any tunable hyperparameters. Finally, we train the target model with the filtered pseudo labels with regularization from the pre-trained source model. Surprisingly, without direct usage of labeled source samples, our PrDA outperforms conventional domain adaptation methods on benchmark datasets. Our code is publicly available at https://github.com/youngryan1993/SFDA-SourceFreeDA
