论文标题
部分可观测时空混沌系统的无模型预测
Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation
论文作者
论文摘要
无监督的域适应性（UDA）旨在使在标记的源域上训练的模型适应未标记的目标域。在本文中，我们提出了原型对比度适应（PROCA），这是一种无监督域自适应语义分割的简单有效的对比度学习方法。以前的域适应方法仅考虑在各个领域的阶层内表示分布的对准，而阶层间结构关系的探索不足，从而导致目标域上的对齐表示可能不再像在源域上那样容易歧视。取而代之的是，ProCA将类间信息纳入班级原型，并采用以类中心的分配对准进行适应。通过将与阳性和其他类原型的同一类原型视为实现以班级分配对齐方式的负面影响，Proca在经典领域适应任务上实现了最先进的表现，{\ em I.E.，gta5 $ \ $ cityscapes \ cityScapes \ to toxsscapes \ text {and} synthia $ \ to $ CityScapeS}。代码可在\ href {https://github.com/jiangzhengkai/proca} {proca}获得。
Unsupervised Domain Adaptation (UDA) aims to adapt the model trained on the labeled source domain to an unlabeled target domain. In this paper, we present Prototypical Contrast Adaptation (ProCA), a simple and efficient contrastive learning method for unsupervised domain adaptive semantic segmentation. Previous domain adaptation methods merely consider the alignment of the intra-class representational distributions across various domains, while the inter-class structural relationship is insufficiently explored, resulting in the aligned representations on the target domain might not be as easily discriminated as done on the source domain anymore. Instead, ProCA incorporates inter-class information into class-wise prototypes, and adopts the class-centered distribution alignment for adaptation. By considering the same class prototypes as positives and other class prototypes as negatives to achieve class-centered distribution alignment, ProCA achieves state-of-the-art performance on classical domain adaptation tasks, {\em i.e., GTA5 $\to$ Cityscapes \text{and} SYNTHIA $\to$ Cityscapes}. Code is available at \href{https://github.com/jiangzhengkai/ProCA}{ProCA}
