# Reimagining Application User Interface (UI) Design using Deep Learning Methods: Challenges and Opportunities

arXiv: https://arxiv.org/abs/2303.13055 · HTML: https://mlsanity.com/paper/2303.13055/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 123 references that are in the map.
- First posted on arXiv 23 Mar 2023.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [MirrorGAN: Learning Text-to-image Generation by Redescription](https://mlsanity.com/paper/1903.05854/) (2019)
- [LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators](https://mlsanity.com/paper/1901.06767/) (2019)
- [Activation Functions: Comparison of trends in Practice and Research for Deep Learning](https://mlsanity.com/paper/1811.03378/) (2018)
- [Neural Design Network: Graphic Layout Generation with Constraints](https://mlsanity.com/paper/1912.09421/) (2019)
- [Convolutional Character Networks](https://mlsanity.com/paper/1910.07954/) (2019)
- [READ: Recursive Autoencoders for Document Layout Generation](https://mlsanity.com/paper/1909.00302/) (2019)
- [Machine Learning-Based Prototyping of Graphical User Interfaces for Mobile Apps](https://mlsanity.com/paper/1802.02312/) (2018)
- [Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology](https://mlsanity.com/paper/1809.03006/) (2018)
- [Object Detection for Graphical User Interface: Old Fashioned or Deep Learning or a Combination?](https://mlsanity.com/paper/2008.05132/) (2020)
- [Learning Visual Importance for Graphic Designs and Data Visualizations](https://mlsanity.com/paper/1708.02660/) (2017)
- [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)
- [Sketchforme: Composing Sketched Scenes from Text Descriptions for Interactive Applications](https://mlsanity.com/paper/1904.04399/) (2019)
- [Serif or Sans: Visual Font Analytics on Book Covers and Online Advertisements](https://mlsanity.com/paper/1906.10269/) (2019)
- [Autofolding for Source Code Summarization](https://mlsanity.com/paper/1403.4503/) (2014)
- [Convolutional Neural Networks for Font Classification](https://mlsanity.com/paper/1708.03669/) (2017)
- [Detecting and Summarizing GUI Changes in Evolving Mobile Apps](https://mlsanity.com/paper/1807.09440/) (2018)
- [Do we agree on user interface aesthetics of Android apps?](https://mlsanity.com/paper/1812.09049/) (2018)
- [Targeted Example Generation for Compilation Errors](https://mlsanity.com/paper/1909.00769/) (2019)
- [Mining Behavioral Patterns from Millions of Android Users](https://mlsanity.com/paper/1702.05060/) (2017)
- [Learning to Infer User Interface Attributes from Images](https://mlsanity.com/paper/1912.13243/) (2019)

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

- [CoGen: Creation of Reusable UI Components in Figma via Textual Commands](https://mlsanity.com/paper/2601.10536/) (2026)

## Abstract (arXiv)

In this paper, we present a review of the recent work in deep learning methods for user interface design. The survey encompasses well known deep learning techniques (deep neural networks, convolutional neural networks, recurrent neural networks, autoencoders, and generative adversarial networks) and datasets widely used to design user interface applications. We highlight important problems and emerging research frontiers in this field. We believe that the use of deep learning for user interface design automation tasks could be one of the high potential fields for the advancement of the software development industry.