论文标题
具有内部和视图对比度学习的区域嵌入
Region Embedding with Intra and Inter-View Contrastive Learning
论文作者
论文摘要
无监督的区域表示学习旨在从未标记的城市数据中提取密集而有效的特征。尽管已经根据多种视图做出了一些努力来解决此问题，但现有方法仍然不足以在视图和/或从不同视图中合并表示形式中提取表示形式。通过遵循两个指南：i）将对比度学习的对比度学习成功进行表示形式学习，以将其用于多视图区域表示和设计一种名为REMVC（带有多视图对比度学习的区域嵌入的区域嵌入）的模型：i）将两个指南进行比较：i）将一个区域与其他视图中的一个区域进行比较，以有效地提取和ii）与其他视图进行比较。我们设计了视图对比度学习模块，该模块有助于学习划定的区域嵌入和视图对比度学习模块，该模块是一种软性共同规范器，以限制嵌入参数和跨多视图的转移知识。我们在两个名为“土地使用聚类”和“地区受欢迎程度预测”的下游任务中利用了博学的区域嵌入。广泛的实验表明，与七种最先进的基线方法相比，我们的模型可以取得令人印象深刻的改进，而在土地使用聚类任务中，利润率超过30％。
Unsupervised region representation learning aims to extract dense and effective features from unlabeled urban data. While some efforts have been made for solving this problem based on multiple views, existing methods are still insufficient in extracting representations in a view and/or incorporating representations from different views. Motivated by the success of contrastive learning for representation learning, we propose to leverage it for multi-view region representation learning and design a model called ReMVC (Region Embedding with Multi-View Contrastive Learning) by following two guidelines: i) comparing a region with others within each view for effective representation extraction and ii) comparing a region with itself across different views for cross-view information sharing. We design the intra-view contrastive learning module which helps to learn distinguished region embeddings and the inter-view contrastive learning module which serves as a soft co-regularizer to constrain the embedding parameters and transfer knowledge across multi-views. We exploit the learned region embeddings in two downstream tasks named land usage clustering and region popularity prediction. Extensive experiments demonstrate that our model achieves impressive improvements compared with seven state-of-the-art baseline methods, and the margins are over 30% in the land usage clustering task.
