论文标题
Alignnet：无监督的实体对齐
AlignNet: Unsupervised Entity Alignment
论文作者
论文摘要
最近开发的深度学习模型能够在没有监督的情况下学习将场景细分为组件对象。这打开了许多新的和令人兴奋的研究途径，使代理可以将对象（或实体）作为输入，而不是像素。不幸的是，尽管这些模型提供了单个帧的出色分割，但它们并没有跟踪一个时间步长分割（或对齐）与以后的时间步长相对应（或对齐）。对齐（或对应）问题阻碍了在下游任务中使用对象表示的进展。在本文中，我们采取步骤解决对齐问题，并提出一个无监督的对准模块对齐网。
Recently developed deep learning models are able to learn to segment scenes into component objects without supervision. This opens many new and exciting avenues of research, allowing agents to take objects (or entities) as inputs, rather that pixels. Unfortunately, while these models provide excellent segmentation of a single frame, they do not keep track of how objects segmented at one time-step correspond (or align) to those at a later time-step. The alignment (or correspondence) problem has impeded progress towards using object representations in downstream tasks. In this paper we take steps towards solving the alignment problem, presenting the AlignNet, an unsupervised alignment module.
