论文标题
无内置的班级条件域对准无监督域的适应性
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
论文作者
论文摘要
我们提出了一种无监督的域适应性方法 - - 从阶级条件的域一致性角度来看，重点是对域内阶级失衡和阶级分布之间的转移的实际考虑。类条件域对准的当前方法旨在根据目标结构域的伪标签估计来显式最大程度地减少损失函数。但是，这些方法以误差积累的形式遭受伪标记偏置的影响。我们提出了一种方法，该方法可以直接从伪标签中明确优化模型参数。取而代之的是，我们提出了一种基于抽样的隐式对准方法，其中样本选择程序由伪标签隐式指导。理论分析揭示了在未对准的类别中存在域 - 歧视捷径的捷径，这是由提出的隐式对准方法来解决的，以促进域 - 反向学习。经验结果和消融研究证实了所提出的方法的有效性，尤其是在存在域内类失衡和域间分布之间的变化的情况下。
We present an approach for unsupervised domain adaptation---with a strong focus on practical considerations of within-domain class imbalance and between-domain class distribution shift---from a class-conditioned domain alignment perspective. Current methods for class-conditioned domain alignment aim to explicitly minimize a loss function based on pseudo-label estimations of the target domain. However, these methods suffer from pseudo-label bias in the form of error accumulation. We propose a method that removes the need for explicit optimization of model parameters from pseudo-labels directly. Instead, we present a sampling-based implicit alignment approach, where the sample selection procedure is implicitly guided by the pseudo-labels. Theoretical analysis reveals the existence of a domain-discriminator shortcut in misaligned classes, which is addressed by the proposed implicit alignment approach to facilitate domain-adversarial learning. Empirical results and ablation studies confirm the effectiveness of the proposed approach, especially in the presence of within-domain class imbalance and between-domain class distribution shift.
