论文标题
DEEPVA：通过语义互动和深度学习桥接认知和计算
DeepVA: Bridging Cognition and Computation through Semantic Interaction and Deep Learning
论文作者
论文摘要
本文探讨了与传统工程特征相比，深度学习（DL）表示如何支持视觉分析中的语义互动（SI）。 SI试图根据数据功能通过与数据项的互动来建模用户的认知推理。我们假设DL表示包含有意义的高级抽象，可以更好地捕获用户的高级认知意图。为了弥合视觉分析中认知和计算之间的差距，我们提出了DeepVA（Deep Visual Analytics），该DeepVA使用高级深度学习表示语义互动而不是低级手工制作的数据功能。为了评估DEEPVA并与具有低级功能的SI模型进行比较，我们设计和实施了一个系统，该系统扩展了传统的Si管道，其功能在三种不同的抽象级别上。为了测试SI的任务抽象和特征抽象之间的关系，我们使用具有三个不同特征抽象级别的语义交互在三个不同的任务抽象级别执行视觉概念学习任务。 DEEPVA有效地加速了数据的认知理解和计算建模之间的交互性收敛，尤其是在高抽象任务中。
This paper examines how deep learning (DL) representations, in contrast to traditional engineered features, can support semantic interaction (SI) in visual analytics. SI attempts to model user's cognitive reasoning via their interaction with data items, based on the data features. We hypothesize that DL representations contain meaningful high-level abstractions that can better capture users' high-level cognitive intent. To bridge the gap between cognition and computation in visual analytics, we propose DeepVA (Deep Visual Analytics), which uses high-level deep learning representations for semantic interaction instead of low-level hand-crafted data features. To evaluate DeepVA and compare to SI models with lower-level features, we design and implement a system that extends a traditional SI pipeline with features at three different levels of abstraction. To test the relationship between task abstraction and feature abstraction in SI, we perform visual concept learning tasks at three different task abstraction levels, using semantic interaction with three different feature abstraction levels. DeepVA effectively hastened interactive convergence between cognitive understanding and computational modeling of the data, especially in high abstraction tasks.
