  The layout of a mobile screen is a critical data source for UI design
research and semantic understanding of the screen. However, UI layouts in
existing datasets are often noisy, have mismatches with their visual
representation, or consists of generic or app-specific types that are difficult
to analyze and model. In this paper, we propose the CLAY pipeline that uses a
deep learning approach for denoising UI layouts, allowing us to automatically
improve existing mobile UI layout datasets at scale. Our pipeline takes both
the screenshot and the raw UI layout, and annotates the raw layout by removing
incorrect nodes and assigning a semantically meaningful type to each node. To
experiment with our data-cleaning pipeline, we create the CLAY dataset of
59,555 human-annotated screen layouts, based on screenshots and raw layouts
from Rico, a public mobile UI corpus. Our deep models achieve high accuracy
with F1 scores of 82.7% for detecting layout objects that do not have a valid
visual representation and 85.9% for recognizing object types, which
significantly outperforms a heuristic baseline. Our work lays a foundation for
creating large-scale high quality UI layout datasets for data-driven mobile UI
research and reduces the need of manual labeling efforts that are prohibitively
expensive.
