# Detecting Visual Design Principles in Art and Architecture through Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2108.04048 · HTML: https://mlsanity.com/paper/2108.04048/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 101 references that are in the map.
- First posted on arXiv 9 Aug 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms](https://mlsanity.com/paper/1706.07068/) (2017)
- [Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future](https://mlsanity.com/paper/2001.07092/) (2020)
- [Composition-Aware Image Aesthetics Assessment](https://mlsanity.com/paper/1907.10801/) (2019)
- [Learning Photography Aesthetics with Deep CNNs](https://mlsanity.com/paper/1707.03981/) (2017)
- [Deep Learning Architect: Classification for Architectural Design through the Eye of Artificial Intelligence](https://mlsanity.com/paper/1812.01714/) (2018)
- [A deep architecture for unified aesthetic prediction](https://mlsanity.com/paper/1708.04890/) (2017)
- [Evaluating CNNs on the Gestalt Principle of Closure](https://mlsanity.com/paper/1904.00285/) (2019)
- [Evaluation of Deep Learning on an Abstract Image Classification Dataset](https://mlsanity.com/paper/1708.07770/) (2017)
- [Engineering Deep Representations for Modeling Aesthetic Perception](https://mlsanity.com/paper/1605.07699/) (2016)

## Built on by (most cited first, 4 of 4)

- [Artificial Intelligence Studies in Cartography: A Review and Synthesis of Methods, Applications, and Ethics](https://mlsanity.com/paper/2312.07901/) (2023)
- [RLSEP: Learning Label Ranks for Multi-label Classification](https://mlsanity.com/paper/2212.04022/) (2022)
- [GaussianMLR: Learning Implicit Class Significance via Calibrated Multi-Label Ranking](https://mlsanity.com/paper/2303.03907/) (2023)
- [UniMLR: Modeling Implicit Class Significance for Multi-Label Ranking](https://mlsanity.com/paper/2508.21772/) (2025)

## Abstract (arXiv)

Visual design is associated with the use of some basic design elements and principles. Those are applied by the designers in the various disciplines for aesthetic purposes, relying on an intuitive and subjective process. Thus, numerical analysis of design visuals and disclosure of the aesthetic value embedded in them are considered as hard. However, it has become possible with emerging artificial intelligence technologies. This research aims at a neural network model, which recognizes and classifies the design principles over different domains. The domains include artwork produced since the late 20th century; professional photos; and facade pictures of contemporary buildings. The data collection and curation processes, including the production of computationally-based synthetic dataset, is genuine. The proposed model learns from the knowledge of myriads of original designs, by capturing the underlying shared patterns. It is expected to consolidate design processes by providing an aesthetic evaluation of the visual compositions with objectivity.