论文标题
将对抗性学习与选择性投票策略耦合，以在部分领域适应中分配对齐
Coupling Adversarial Learning with Selective Voting Strategy for Distribution Alignment in Partial Domain Adaptation
论文作者
论文摘要
与标准的闭合域的适应任务相反，部分域适应设置可以通过放松相同的标签集假设来迎合现实情况。但是，源标签集集成了目标标签集的事实，但是，由于私人源类别样本的培训阻止了相关的知识转移并误导分类过程，因此引入了一些其他障碍。为了减轻这些问题，我们设计了一种机制，用于策略选择高度自信的目标样本，这对于估计班级重要性权重必不可少。此外，我们通过将实现紧凑型和不同类别分布的过程与对抗性目标结合过程来捕获阶级歧视和域不变特征。对众多跨域分类任务的实验发现证明了所提出的技术的潜力，比现有方法提供了卓越的准确性。
In contrast to a standard closed-set domain adaptation task, partial domain adaptation setup caters to a realistic scenario by relaxing the identical label set assumption. The fact of source label set subsuming the target label set, however, introduces few additional obstacles as training on private source category samples thwart relevant knowledge transfer and mislead the classification process. To mitigate these issues, we devise a mechanism for strategic selection of highly-confident target samples essential for the estimation of class-importance weights. Furthermore, we capture class-discriminative and domain-invariant features by coupling the process of achieving compact and distinct class distributions with an adversarial objective. Experimental findings over numerous cross-domain classification tasks demonstrate the potential of the proposed technique to deliver superior and comparable accuracy over existing methods.
