  UI design is an integral part of software development. For many developers
who do not have much UI design experience, exposing them to a large database of
real-application UI designs can help them quickly build up a realistic
understanding of the design space for a software feature and get design
inspirations from existing applications. However, existing keyword-based,
image-similarity-based, and component-matching-based methods cannot reliably
find relevant high-fidelity UI designs in a large database alike to the UI
wireframe that the developers sketch, in face of the great variations in UI
designs. In this article, we propose a deep-learning-based UI design search
engine to fill in the gap. The key innovation of our search engine is to train
a wireframe image autoencoder using a large database of real-application UI
designs, without the need for labeling relevant UI designs. We implement our
approach for Android UI design search, and conduct extensive experiments with
artificially created relevant UI designs and human evaluation of UI design
search results. Our experiments confirm the superior performance of our search
engine over existing image-similarity or component-matching-based methods and
demonstrate the usefulness of our search engine in real-world UI design tasks.
