论文标题
通过正交和聚类的嵌入在语义分割中无监督的域的适应
Unsupervised Domain Adaptation in Semantic Segmentation via Orthogonal and Clustered Embeddings
论文作者
论文摘要
深度学习框架允许在语义细分方面取得显着的进步，但是卷积网络的饥饿性质迅速提高了对能够从标签丰富域中转移学识渊博的知识的适应技术的需求。在本文中，我们提出了一种有效的无监督域适应性（UDA）策略，该策略基于特征聚类方法，该方法将特征分布的不同语义模式和同一类特征的不同语义模式捕获成紧密且分离良好的群集。此外，我们介绍了两个新颖的学习目标，以增强判别性聚类性能：正交性损失力使单个表示形式伸出是正交的，而稀疏性损失则减少了班级，从而减少了活动特征通道的数量。这些模块的关节效应是使特征空间的结构正规化。在综合到实现的情况下进行了广泛的评估表明，我们实现了最先进的绩效。
Deep learning frameworks allowed for a remarkable advancement in semantic segmentation, but the data hungry nature of convolutional networks has rapidly raised the demand for adaptation techniques able to transfer learned knowledge from label-abundant domains to unlabeled ones. In this paper we propose an effective Unsupervised Domain Adaptation (UDA) strategy, based on a feature clustering method that captures the different semantic modes of the feature distribution and groups features of the same class into tight and well-separated clusters. Furthermore, we introduce two novel learning objectives to enhance the discriminative clustering performance: an orthogonality loss forces spaced out individual representations to be orthogonal, while a sparsity loss reduces class-wise the number of active feature channels. The joint effect of these modules is to regularize the structure of the feature space. Extensive evaluations in the synthetic-to-real scenario show that we achieve state-of-the-art performance.
