Universal domain adaptation (UniDA) aims to transfer the knowledge of common classes from source domain to target domain without any prior knowledge on the label set, which requires to distinguish the unknown samples from the known ones in the target domain. Like the traditional unsupervised domain adaptation problem, the misalignment between two domains exists due to the biased and less-discriminative embedding in target domain. Recent methods proposed to complete the domain misalignment by clustering target samples with the nearest neighbors or nearest prototypes. However, it is dangerous to do so because both known and unknown samples may distribute on the edges of source clusters. Meanwhile, other existing classifier-based methods could easily produce overconfident predictions for unknown samples because the supervised objectives in source domain leads the whole model to be biased towards the common classes. Therefore, to deal with the first issue, we propose to exploit the distribution of target samples and introduce an empirical estimation of the probability of a target sample belong to the unknown class. Then, based on the estimation, we propose a novel unknown samples discovering method in the linear subspace with a $\delta$-filter to estimate the uncertainty of each target sample, which can fully exploit the relationship between the target sample and its neighbors. Moreover, for the second issue, this paper well balances the confidence values of both known and unknown samples through an uncertainty-guided margin loss. It enforces a margin to source samples to encourage a similar intra-class variance of source samples to that of unknown samples.
翻译:通用域适应(UniDA)旨在将共同类别知识从源域转移到目标域,而没有事先对标签组的任何知识,这就要求将未知样本与目标域已知样本区分开来。像传统的不受监督的域适应问题一样,由于目标域存在偏差和较少偏差的嵌入,两个领域之间存在不匹配问题。最近建议的方法是通过将目标样品与最近的邻居或最近的原型组合成目标样品来完成域的错配。然而,这样做是危险的,因为已知和未知的样品可能在来源组的边缘上分布。与此同时,其他现有的基于分类的各种方法很容易对未知样本作出过于自信的预测,因为受监督的源域目标域的目标区域的目标区域使整个模型偏向共同类别。因此,为了处理第一个问题,我们提议利用目标样品的分布,对目标样品的概率的概率进行实证估计,对未知的概率属于未知类别。然后,根据估计,我们提出一种新的未知的样品在线性次空间底部的底部发现方法,用美元-delta美元-fellter的底部,对未知的底部,对未知的底部的底部进行估计。为了估计,每个目标样品的底部的底部的底部的底部,可以利用一个未知的底部的底部的底部的底部的样品来估计,对一个未知的底部的底部的底部,从而估计,对未知的底部的底部的样品,对未知的底部的底部的底部的底部,可以利用一个未知的底部的底部的底部的底部的底部的底部的底部的底部的底部的底部的样品,用来评估。一个未知的底部的底部的底部的底部。