# Pairwise GUI Dataset Construction Between Android Phones and Tablets

arXiv: https://arxiv.org/abs/2310.04755 · HTML: https://mlsanity.com/paper/2310.04755/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 45 references that are in the map.
- First posted on arXiv 7 Oct 2023.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [A note on the evaluation of generative models](https://mlsanity.com/paper/1511.01844/) (2015)
- [Statistical Aspects of Wasserstein Distances](https://mlsanity.com/paper/1806.05500/) (2018)
- [LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators](https://mlsanity.com/paper/1901.06767/) (2019)
- [LayoutTransformer: Layout Generation and Completion with Self-attention](https://mlsanity.com/paper/2006.14615/) (2020)
- [Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning](https://mlsanity.com/paper/2108.03353/) (2021)
- [LayoutVAE: Stochastic Scene Layout Generation From a Label Set](https://mlsanity.com/paper/1907.10719/) (2019)
- [Variational Transformer Networks for Layout Generation](https://mlsanity.com/paper/2104.02416/) (2021)
- [READ: Recursive Autoencoders for Document Layout Generation](https://mlsanity.com/paper/1909.00302/) (2019)
- [VINS: Visual Search for Mobile User Interface Design](https://mlsanity.com/paper/2102.05216/) (2021)
- [Learning to Denoise Raw Mobile UI Layouts for Improving Datasets at Scale](https://mlsanity.com/paper/2201.04100/) (2022)
- [Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning](https://mlsanity.com/paper/2003.00380/) (2020)
- [Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps](https://mlsanity.com/paper/2101.04401/) (2021)
- [Wireframe-Based UI Design Search Through Image Autoencoder](https://mlsanity.com/paper/2103.07085/) (2021)
- [GUIComp: A GUI Design Assistant with Real-Time, Multi-Faceted Feedback](https://mlsanity.com/paper/2001.05684/) (2020)
- [ORCSolver: An Efficient Solver for Adaptive GUI Layout with OR-Constraints](https://mlsanity.com/paper/2002.09925/) (2020)
- [ReverseORC: Reverse Engineering of Resizable User Interface Layouts with OR-Constraints](https://mlsanity.com/paper/2202.11523/) (2022)
- [A First Look at On-device Models in iOS Apps](https://mlsanity.com/paper/2307.12328/) (2023)
- [Automated Mapping of Adaptive App GUIs from Phones to TVs](https://mlsanity.com/paper/2307.12522/) (2023)

## Built on by (most cited first, 1 of 1)

- [OpenHarmony Bench: Evaluating LLMs and Coding Agents on OpenHarmony App Development](https://mlsanity.com/paper/2608.16022/) (2026)

## Abstract (arXiv)

In the current landscape of pervasive smartphones and tablets, apps frequently exist across both platforms. Although apps share most graphic user interfaces (GUIs) and functionalities across phones and tablets, developers often rebuild from scratch for tablet versions, escalating costs and squandering existing design resources. Researchers are attempting to collect data and employ deep learning in automated GUIs development to enhance developers' productivity. There are currently several publicly accessible GUI page datasets for phones, but none for pairwise GUIs between phones and tablets. This poses a significant barrier to the employment of deep learning in automated GUI development. In this paper, we introduce the Papt dataset, a pioneering pairwise GUI dataset tailored for Android phones and tablets, encompassing 10,035 phone-tablet GUI page pairs sourced from 5,593 unique app pairs. We propose novel pairwise GUI collection approaches for constructing this dataset and delineate its advantages over currently prevailing datasets in the field. Through preliminary experiments on this dataset, we analyze the present challenges of utilizing deep learning in automated GUI development.