论文标题
通过许多分类器，无监督的域适应源的域间隙估计
Domain Gap Estimation for Source Free Unsupervised Domain Adaptation with Many Classifiers
论文作者
论文摘要
从理论上讲，无监督的域适应性（UDA）的成功在很大程度上取决于域间隙估计。但是，对于无源UDA，在适应过程中无法访问源域数据，这在测量域间隙方面构成了巨大的挑战。在本文中，我们建议使用许多分类器来学习源域决策边界，即使两个域数据无法同时访问，它也提供了域间隙的更紧密的上限。对源模型进行了训练，可以推开每对分类器，同时确保决策边界的正确性。从这个意义上讲，我们的许多分类器模型尽可能将源不同类别分开，从而诱导目标域中许多分类器的最大分歧，从而最大程度地提高了可转移的源域知识。为了进行适应，源模型可用于最大化分类器对之间的一致性。因此，目标特征从决策边界脱颖而出。 UDA的几个数据集的实验表明，我们的方法在源免费的UDA方法之间达到了最先进的性能，甚至可以竞争用于源可用的UDA方法。
In theory, the success of unsupervised domain adaptation (UDA) largely relies on domain gap estimation. However, for source free UDA, the source domain data can not be accessed during adaptation, which poses great challenge of measuring the domain gap. In this paper, we propose to use many classifiers to learn the source domain decision boundaries, which provides a tighter upper bound of the domain gap, even if both of the domain data can not be simultaneously accessed. The source model is trained to push away each pair of classifiers whilst ensuring the correctness of the decision boundaries. In this sense, our many classifiers model separates the source different categories as far as possible which induces the maximum disagreement of many classifiers in the target domain, thus the transferable source domain knowledge is maximized. For adaptation, the source model is adapted to maximize the agreement among pairs of the classifiers. Thus the target features are pushed away from the decision boundaries. Experiments on several datasets of UDA show that our approach achieves state of the art performance among source free UDA approaches and can even compete to source available UDA methods.
