一种基于分类器paradigm的通用领域自适应自适应未知认证方法
Towards Adaptive Unknown Authentication for Universal Domain Adaptation by Classifier Paradox
摘要：
通用领域自适应（UniDA）是一种通用的无监督领域自适应设置，其解决自适应中的领域和标签偏移。它的主要挑战在于如何识别非共享或未知类中的目标样本。以前的方法通常致力于描述样本的“置信度”以及用于拒绝未知的阈值，并跨域对齐共享类的特征分布。然而，它仍然很难预先指定一个“信心”的标准和阈值，这是适应各种真实的任务，和未知数的错误预测进一步招致的不对齐的功能在共享类。在本文中，我们提出了一种新的UniDA方法与自适应未知认证的分类器paradigm（UACP），考虑到样本与矛盾的预测可能是未知的不属于源类。在UACP中，复合分类器是由两种类型的预测器联合设计的。也就是说，多类（MC）预测器将样本分类到多个源类中的一个，而二进制一对全（OVA）预测器进一步验证MC预测器的预测。验证失败或悖论的样本被识别为未知数。此外，代替共享类的特征对齐，在输出空间中进行隐式域对齐，使得跨域的样本共享相同的决策边界，尽管具有特征差异。实证结果验证了UACP在开集和通用UDA设置。
英文摘要：
Universal domain adaptation (UniDA) is a general unsupervised domain adaptation setting, which addresses both domain and label shifts in adaptation. Its main challenge lies in how to identify target samples in unshared or unknown classes. Previous methods commonly strive to depict sample "confidence" along with a threshold for rejecting unknowns, and align feature distributions of shared classes across domains. However, it is still hard to pre-specify a "confidence" criterion and threshold which are adaptive to various real tasks, and a mis-prediction of unknowns further incurs misalignment of features in shared classes. In this paper, we propose a new UniDA method with adaptive Unknown Authentication by Classifier Paradox (UACP), considering that samples with paradoxical predictions are probably unknowns belonging to none of the source classes. In UACP, a composite classifier is jointly designed with two types of predictors. That is, a multi-class (MC) predictor classifies samples to one of the multiple source classes, while a binary one-vs-all (OVA) predictor further verifies the prediction by MC predictor. Samples with verification failure or paradox are identified as unknowns. Further, instead of feature alignment for shared classes, implicit domain alignment is conducted in output space such that samples across domains share the same decision boundary, though with feature discrepancy. Empirical results validate UACP under both open-set and universal UDA settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
